/1. Part 1 Introduction/
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1. A Practical Example What You Will Learn in This Course.mp4
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51.4 MB
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1. A Practical Example What You Will Learn in This Course.srt
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6.5 KB
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2. What Does the Course Cover.mp4
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65.3 MB
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2. What Does the Course Cover.srt
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5.2 KB
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3. Download All Resources and Important FAQ.html
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21.9 KB
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3.1 FAQ_The_Data_Science_Course.pdf.pdf
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313.4 KB
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3.2 Download All Resources.html
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0.1 KB
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/10. Probability - Combinatorics/
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1. Fundamentals of Combinatorics.mp4
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17.0 MB
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1. Fundamentals of Combinatorics.srt
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1.3 KB
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1.1 Course Notes - Combinatorics.pdf.pdf
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231.5 KB
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10. Solving Variations without Repetition.html
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0.2 KB
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11. Solving Combinations.mp4
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60.1 MB
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11. Solving Combinations.srt
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5.7 KB
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11.1 Combinations With Repetition.pdf.pdf
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212.4 KB
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12. Solving Combinations.html
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0.2 KB
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13. Symmetry of Combinations.mp4
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42.3 MB
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13. Symmetry of Combinations.srt
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4.4 KB
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13.1 Symmetry Explained.pdf.pdf
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87.1 KB
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14. Symmetry of Combinations.html
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0.2 KB
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15. Solving Combinations with Separate Sample Spaces.mp4
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34.8 MB
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15. Solving Combinations with Separate Sample Spaces.srt
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3.8 KB
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16. Solving Combinations with Separate Sample Spaces.html
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0.2 KB
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17. Combinatorics in Real-Life The Lottery.mp4
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43.3 MB
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17. Combinatorics in Real-Life The Lottery.srt
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4.2 KB
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18. Combinatorics in Real-Life The Lottery.html
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0.2 KB
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19. A Recap of Combinatorics.mp4
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40.4 MB
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19. A Recap of Combinatorics.srt
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3.8 KB
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2. Fundamentals of Combinatorics.html
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0.2 KB
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20. A Practical Example of Combinatorics.mp4
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140.8 MB
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20. A Practical Example of Combinatorics.srt
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14.3 KB
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20.1 Additional Exercises Combinatorics.pdf.pdf
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109.1 KB
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20.2 Additional Exercises Combinatorics Solutions.pdf.pdf
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251.6 KB
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3. Permutations and How to Use Them.mp4
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44.8 MB
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3. Permutations and How to Use Them.srt
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4.2 KB
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4. Permutations and How to Use Them.html
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0.2 KB
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5. Simple Operations with Factorials.mp4
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37.9 MB
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5. Simple Operations with Factorials.srt
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3.3 KB
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6. Simple Operations with Factorials.html
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0.2 KB
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7. Solving Variations with Repetition.mp4
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35.7 MB
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7. Solving Variations with Repetition.srt
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3.6 KB
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8. Solving Variations with Repetition.html
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0.2 KB
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9. Solving Variations without Repetition.mp4
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45.2 MB
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9. Solving Variations without Repetition.srt
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45.2 MB
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/11. Probability - Bayesian Inference/
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1. Sets and Events.mp4
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56.1 MB
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1. Sets and Events.srt
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5.2 KB
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1.1 Course Notes - Bayesian Inference.pdf.pdf
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395.3 KB
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10. Mutually Exclusive Sets.html
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0.2 KB
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11. Dependence and Independence of Sets.mp4
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36.5 MB
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11. Dependence and Independence of Sets.srt
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3.5 KB
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12. Dependence and Independence of Sets.html
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0.2 KB
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13. The Conditional Probability Formula.mp4
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48.1 MB
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13. The Conditional Probability Formula.srt
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5.1 KB
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14. The Conditional Probability Formula.html
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0.2 KB
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15. The Law of Total Probability.mp4
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36.6 MB
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15. The Law of Total Probability.srt
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3.6 KB
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16. The Additive Rule.mp4
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28.3 MB
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16. The Additive Rule.srt
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2.8 KB
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17. The Additive Rule.html
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0.2 KB
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18. The Multiplication Law.mp4
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51.4 MB
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18. The Multiplication Law.srt
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4.7 KB
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19. The Multiplication Law.html
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0.2 KB
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2. Sets and Events.html
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0.2 KB
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20. Bayes' Law.mp4
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52.4 MB
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20. Bayes' Law.srt
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7.4 KB
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21. Bayes' Law.html
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0.2 KB
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22. A Practical Example of Bayesian Inference.mp4
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152.2 MB
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22. A Practical Example of Bayesian Inference.srt
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19.8 KB
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22.1 CDS_2017-2018 Hamilton.pdf.pdf
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865.6 KB
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22.2 Bayesian Homework - Solutions.pdf.pdf
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31.1 KB
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22.3 Bayesian Homework .pdf.pdf
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27.9 KB
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3. Ways Sets Can Interact.mp4
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49.7 MB
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3. Ways Sets Can Interact.srt
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4.5 KB
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4. Ways Sets Can Interact.html
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0.2 KB
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5. Intersection of Sets.mp4
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28.3 MB
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5. Intersection of Sets.srt
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2.5 KB
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6. Intersection of Sets.html
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0.2 KB
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7. Union of Sets.mp4
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60.0 MB
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7. Union of Sets.srt
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5.7 KB
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8. Union of Sets.html
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0.2 KB
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9. Mutually Exclusive Sets.mp4
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26.6 MB
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9. Mutually Exclusive Sets.srt
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2.6 KB
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/12. Probability - Distributions/
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1. Fundamentals of Probability Distributions.mp4
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77.0 MB
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1. Fundamentals of Probability Distributions.srt
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7.7 KB
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1.1 Course Notes - Probability Distributions.pdf.pdf
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475.1 KB
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10. Discrete Distributions The Bernoulli Distribution.html
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0.2 KB
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11. Discrete Distributions The Binomial Distribution.mp4
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72.2 MB
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11. Discrete Distributions The Binomial Distribution.srt
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8.5 KB
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12. Discrete Distributions The Binomial Distribution.html
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0.2 KB
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13. Discrete Distributions The Poisson Distribution.mp4
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58.5 MB
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13. Discrete Distributions The Poisson Distribution.srt
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6.7 KB
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13.1 Poisson - Expected Value and Variance.pdf.pdf
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149.5 KB
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14. Discrete Distributions The Poisson Distribution.html
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0.2 KB
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15. Characteristics of Continuous Distributions.mp4
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88.2 MB
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15. Characteristics of Continuous Distributions.srt
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8.9 KB
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15.1 Solving Integrals.pdf.pdf
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352.1 KB
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16. Characteristics of Continuous Distributions.html
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0.2 KB
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17. Continuous Distributions The Normal Distribution.mp4
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50.6 MB
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17. Continuous Distributions The Normal Distribution.srt
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4.9 KB
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17.1 Normal Distribution - Exp and Var.pdf.pdf
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147.5 KB
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18. Continuous Distributions The Normal Distribution.html
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0.2 KB
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19. Continuous Distributions The Standard Normal Distribution.mp4
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50.2 MB
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19. Continuous Distributions The Standard Normal Distribution.srt
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5.4 KB
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2. Fundamentals of Probability Distributions.html
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0.2 KB
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20. Continuous Distributions The Standard Normal Distribution.html
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0.2 KB
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21. Continuous Distributions The Students' T Distribution.mp4
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28.5 MB
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21. Continuous Distributions The Students' T Distribution.srt
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2.9 KB
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22. Continuous Distributions The Students' T Distribution.html
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0.2 KB
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23. Continuous Distributions The Chi-Squared Distribution.mp4
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27.6 MB
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23. Continuous Distributions The Chi-Squared Distribution.srt
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2.8 KB
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24. Continuous Distributions The Chi-Squared Distribution.html
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0.2 KB
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25. Continuous Distributions The Exponential Distribution.mp4
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42.2 MB
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25. Continuous Distributions The Exponential Distribution.srt
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4.2 KB
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26. Continuous Distributions The Exponential Distribution.html
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0.2 KB
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27. Continuous Distributions The Logistic Distribution.mp4
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49.3 MB
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27. Continuous Distributions The Logistic Distribution.srt
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5.1 KB
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28. Continuous Distributions The Logistic Distribution.html
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0.2 KB
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29. A Practical Example of Probability Distributions.mp4
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165.5 MB
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29. A Practical Example of Probability Distributions.srt
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20.4 KB
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29.1 FIFA19.csv.csv
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9.1 MB
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29.2 Customers_Membership (post).xlsx.xlsx
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16.0 KB
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29.3 FIFA19 (post).csv.csv
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9.1 MB
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29.4 Daily Views.xlsx.xlsx
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9.8 KB
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29.5 Customers_Membership.xlsx.xlsx
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9.9 KB
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29.6 Daily Views (post).xlsx.xlsx
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20.7 KB
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3. Types of Probability Distributions.mp4
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96.0 MB
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3. Types of Probability Distributions.srt
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9.7 KB
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4. Types of Probability Distributions.html
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0.2 KB
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5. Characteristics of Discrete Distributions.mp4
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23.8 MB
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5. Characteristics of Discrete Distributions.srt
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2.5 KB
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6. Characteristics of Discrete Distributions.html
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0.2 KB
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7. Discrete Distributions The Uniform Distribution.mp4
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25.6 MB
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7. Discrete Distributions The Uniform Distribution.srt
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2.8 KB
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8. Discrete Distributions The Uniform Distribution.html
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0.2 KB
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9. Discrete Distributions The Bernoulli Distribution.mp4
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35.8 MB
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9. Discrete Distributions The Bernoulli Distribution.srt
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3.9 KB
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/13. Probability - Probability in Other Fields/
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1. Probability in Finance.mp4
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103.9 MB
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1. Probability in Finance.srt
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10.1 KB
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1.1 Probability in Finance Solutions.pdf.pdf
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188.9 KB
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1.2 Probability in Finance Homework.pdf.pdf
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113.3 KB
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2. Probability in Statistics.mp4
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81.0 MB
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2. Probability in Statistics.srt
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8.6 KB
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3. Probability in Data Science.mp4
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66.6 MB
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3. Probability in Data Science.srt
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6.8 KB
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/14. Part 3 Statistics/
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1. Population and Sample.mp4
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60.9 MB
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1. Population and Sample.srt
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5.6 KB
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1.1 Statistics Glossary.xlsx.xlsx
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20.8 KB
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1.2 Course notes_descriptive_statistics.pdf.pdf
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493.8 KB
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2. Population and Sample.html
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0.2 KB
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/15. Statistics - Descriptive Statistics/
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1. Types of Data.mp4
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76.0 MB
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1. Types of Data.srt
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6.1 KB
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1.1 Course notes_descriptive_statistics.pdf.pdf
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493.8 KB
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1.2 Glossary.xlsx.xlsx
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20.4 KB
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10. Numerical Variables Exercise.html
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0.1 KB
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10.1 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx
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12.0 KB
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10.2 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx
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13.5 KB
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11. The Histogram.mp4
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14.4 MB
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11. The Histogram.srt
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3.1 KB
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11.1 2.5. The Histogram_lesson.xlsx.xlsx
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19.1 KB
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12. The Histogram.html
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0.2 KB
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13. Histogram Exercise.html
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0.1 KB
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13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx
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17.5 KB
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13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf
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296.1 KB
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13.3 2.5.The-Histogram-exercise.xlsx.xlsx
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15.9 KB
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14. Cross Tables and Scatter Plots.mp4
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41.7 MB
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14. Cross Tables and Scatter Plots.srt
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6.8 KB
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14.1 2.6. Cross table and scatter plot.xlsx.xlsx
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26.7 KB
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15. Cross Tables and Scatter Plots.html
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0.2 KB
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16. Cross Tables and Scatter Plots Exercise.html
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0.1 KB
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16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx
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16.7 KB
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16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx
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41.4 KB
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17. Mean, median and mode.mp4
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38.9 MB
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17. Mean, median and mode.srt
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5.9 KB
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17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx
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10.7 KB
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18. Mean, Median and Mode Exercise.html
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0.1 KB
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18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx
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11.1 KB
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18.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx
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11.6 KB
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19. Skewness.mp4
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20.3 MB
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19. Skewness.srt
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3.7 KB
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19.1 2.8. Skewness_lesson.xlsx.xlsx
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35.5 KB
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2. Types of Data.html
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0.2 KB
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20. Skewness.html
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0.2 KB
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21. Skewness Exercise.html
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0.1 KB
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21.1 2.8. Skewness_exercise.xlsx.xlsx
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9.7 KB
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21.2 2.8. Skewness_exercise_solution.xlsx.xlsx
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20.3 KB
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22. Variance.mp4
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53.4 MB
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22. Variance.srt
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7.7 KB
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22.1 2.9. Variance_lesson.xlsx.xlsx
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10.3 KB
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23. Variance Exercise.html
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0.5 KB
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23.1 2.9. Variance_exercise.xlsx.xlsx
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11.1 KB
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23.2 2.9. Variance_exercise_solution.xlsx.xlsx
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11.3 KB
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24. Standard Deviation and Coefficient of Variation.mp4
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47.3 MB
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24. Standard Deviation and Coefficient of Variation.srt
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6.8 KB
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24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx
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11.2 KB
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25. Standard Deviation.html
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0.2 KB
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26. Standard Deviation and Coefficient of Variation Exercise.html
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0.1 KB
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26.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx.xlsx
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12.9 KB
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26.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx.xlsx
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11.9 KB
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27. Covariance.mp4
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28.8 MB
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27. Covariance.srt
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5.0 KB
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27.1 2.11. Covariance_lesson.xlsx.xlsx
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25.5 KB
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28. Covariance.html
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0.2 KB
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29. Covariance Exercise.html
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0.1 KB
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29.1 2.11. Covariance_exercise_solution.xlsx.xlsx
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30.2 KB
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29.2 2.11. Covariance_exercise.xlsx.xlsx
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20.7 KB
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3. Levels of Measurement.mp4
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57.0 MB
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3. Levels of Measurement.srt
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4.7 KB
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30. Correlation Coefficient.mp4
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30.8 MB
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30. Correlation Coefficient.srt
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4.8 KB
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31. Correlation.html
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0.2 KB
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32. Correlation Coefficient Exercise.html
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0.1 KB
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32.1 2.12. Correlation_exercise_solution.xlsx.xlsx
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30.2 KB
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32.2 2.12. Correlation_exercise.xlsx.xlsx
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30.0 KB
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4. Levels of Measurement.html
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0.2 KB
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5. Categorical Variables - Visualization Techniques.mp4
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40.3 MB
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5. Categorical Variables - Visualization Techniques.srt
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6.6 KB
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5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx
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31.5 KB
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6. Categorical Variables - Visualization Techniques.html
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0.2 KB
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7. Categorical Variables Exercise.html
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0.1 KB
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7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx
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15.6 KB
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7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf
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296.1 KB
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7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx
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42.1 KB
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8. Numerical Variables - Frequency Distribution Table.mp4
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27.1 MB
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8. Numerical Variables - Frequency Distribution Table.srt
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4.5 KB
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8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx
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11.7 KB
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9. Numerical Variables - Frequency Distribution Table.html
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0.2 KB
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/16. Statistics - Practical Example Descriptive Statistics/
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1. Practical Example Descriptive Statistics.mp4
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168.2 MB
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1. Practical Example Descriptive Statistics.srt
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21.3 KB
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1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx
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150.0 KB
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2. Practical Example Descriptive Statistics Exercise.html
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0.1 KB
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2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx
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149.9 KB
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2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx
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123.2 KB
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/17. Statistics - Inferential Statistics Fundamentals/
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1. Introduction.mp4
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16.2 MB
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1. Introduction.srt
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1.7 KB
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1.1 Course notes_inferential statistics.pdf.pdf
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391.5 KB
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10. Central Limit Theorem.html
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0.2 KB
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11. Standard error.mp4
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23.9 MB
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11. Standard error.srt
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2.1 KB
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12. Standard Error.html
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0.2 KB
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13. Estimators and Estimates.mp4
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50.2 MB
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13. Estimators and Estimates.srt
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3.8 KB
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14. Estimators and Estimates.html
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0.2 KB
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2. What is a Distribution.mp4
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64.6 MB
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2. What is a Distribution.srt
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6.0 KB
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2.1 3.2. What is a distribution_lesson.xlsx.xlsx
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19.9 KB
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2.2 Course notes_inferential statistics.pdf.pdf
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391.5 KB
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3. What is a Distribution.html
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0.2 KB
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4. The Normal Distribution.mp4
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52.3 MB
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4. The Normal Distribution.srt
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5.0 KB
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5. The Normal Distribution.html
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0.2 KB
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6. The Standard Normal Distribution.mp4
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23.6 MB
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6. The Standard Normal Distribution.srt
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4.0 KB
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6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx
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10.6 KB
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7. The Standard Normal Distribution.html
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0.2 KB
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8. The Standard Normal Distribution Exercise.html
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0.1 KB
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8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx
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12.3 KB
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8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx
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24.6 KB
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9. Central Limit Theorem.mp4
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65.9 MB
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9. Central Limit Theorem.srt
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5.8 KB
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/18. Statistics - Inferential Statistics Confidence Intervals/
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1. What are Confidence Intervals.mp4
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52.4 MB
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1. What are Confidence Intervals.srt
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3.3 KB
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10. Margin of Error.mp4
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62.0 MB
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10. Margin of Error.srt
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6.3 KB
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11. Margin of Error.html
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0.2 KB
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12. Confidence intervals. Two means. Dependent samples.mp4
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73.9 MB
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12. Confidence intervals. Two means. Dependent samples.srt
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33.6 MB
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12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx
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10.7 KB
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13. Confidence intervals. Two means. Dependent samples Exercise.html
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0.1 KB
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13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx
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14.1 KB
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13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx
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14.6 KB
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14. Confidence intervals. Two means. Independent samples (Part 1).mp4
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30.2 MB
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14. Confidence intervals. Two means. Independent samples (Part 1).srt
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6.2 KB
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14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx
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10.1 KB
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15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html
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0.1 KB
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15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx
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10.4 KB
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15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx
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10.1 KB
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16. Confidence intervals. Two means. Independent samples (Part 2).mp4
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28.1 MB
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16. Confidence intervals. Two means. Independent samples (Part 2).srt
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4.6 KB
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16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx
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9.7 KB
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17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html
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0.1 KB
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17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx
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9.4 KB
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17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx
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10.0 KB
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18. Confidence intervals. Two means. Independent samples (Part 3).mp4
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20.9 MB
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18. Confidence intervals. Two means. Independent samples (Part 3).srt
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2.0 KB
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2. What are Confidence Intervals.html
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0.2 KB
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3. Confidence Intervals; Population Variance Known; z-score.mp4
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82.0 MB
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3. Confidence Intervals; Population Variance Known; z-score.srt
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10.0 KB
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3.1 3.9. Population variance known, z-score_lesson.xlsx.xlsx
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11.5 KB
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3.2 3.9.The-z-table.xlsx.xlsx
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26.2 KB
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4. Confidence Intervals; Population Variance Known; z-score; Exercise.html
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0.1 KB
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4.1 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx
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11.4 KB
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4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx
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11.1 KB
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4.3 3.9.The-z-table.xlsx.xlsx
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26.2 KB
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5. Confidence Interval Clarifications.mp4
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59.8 MB
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5. Confidence Interval Clarifications.srt
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5.5 KB
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6. Student's T Distribution.mp4
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37.2 MB
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6. Student's T Distribution.srt
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4.2 KB
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7. Student's T Distribution.html
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0.2 KB
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8. Confidence Intervals; Population Variance Unknown; t-score.mp4
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33.8 MB
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8. Confidence Intervals; Population Variance Unknown; t-score.srt
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5.8 KB
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8.1 3.11. The t-table.xlsx.xlsx
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8.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx
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11.0 KB
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9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html
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0.1 KB
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9.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx
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11.4 KB
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9.2 3.11.The-t-table.xlsx.xlsx
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16.2 KB
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/19. Statistics - Practical Example Inferential Statistics/
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1. Practical Example Inferential Statistics.mp4
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107.7 MB
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1. Practical Example Inferential Statistics.srt
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14.0 KB
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1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx
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1.8 MB
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2. Practical Example Inferential Statistics Exercise.html
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0.1 KB
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2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx.xlsx
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1.8 MB
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2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx
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1.9 MB
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/2. The Field of Data Science - The Various Data Science Disciplines/
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1. Data Science and Business Buzzwords Why are there so many.mp4
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85.4 MB
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1. Data Science and Business Buzzwords Why are there so many.srt
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6.8 KB
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10. A Breakdown of our Data Science Infographic.html
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0.2 KB
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2. Data Science and Business Buzzwords Why are there so many.html
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0.2 KB
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3. What is the difference between Analysis and Analytics.mp4
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56.2 MB
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3. What is the difference between Analysis and Analytics.srt
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5.2 KB
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4. What is the difference between Analysis and Analytics.html
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0.2 KB
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5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4
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67.6 MB
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5. Business Analytics, Data Analytics, and Data Science An Introduction.srt
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10.9 KB
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5.1 365_DataScience_Diagram.pdf.pdf
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330.8 KB
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6. Business Analytics, Data Analytics, and Data Science An Introduction.html
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0.2 KB
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7. Continuing with BI, ML, and AI.mp4
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114.3 MB
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12.2 KB
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7.1 365_DataScience_Diagram.pdf.pdf
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330.8 KB
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7.2 365_DataScience.png.png
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7.3 MB
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8. Continuing with BI, ML, and AI.html
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0.2 KB
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9. A Breakdown of our Data Science Infographic.mp4
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71.0 MB
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9. A Breakdown of our Data Science Infographic.srt
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/20. Statistics - Hypothesis Testing/
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1. Null vs Alternative Hypothesis.mp4
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96.5 MB
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1. Null vs Alternative Hypothesis.srt
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7.1 KB
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1.1 Course notes_hypothesis_testing.pdf.pdf
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663.8 KB
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10. p-value.mp4
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58.6 MB
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10. p-value.srt
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58.6 MB
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10.1 Online p-value calculator.pdf.pdf
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1.2 MB
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11. p-value.html
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0.2 KB
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12. Test for the Mean. Population Variance Unknown.mp4
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42.2 MB
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12. Test for the Mean. Population Variance Unknown.srt
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5.9 KB
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12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx
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14.9 KB
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13. Test for the Mean. Population Variance Unknown Exercise.html
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0.1 KB
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13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx
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12.9 KB
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13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx
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11.6 KB
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14. Test for the Mean. Dependent Samples.mp4
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52.8 MB
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14. Test for the Mean. Dependent Samples.srt
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6.4 KB
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14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx
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10.0 KB
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15. Test for the Mean. Dependent Samples Exercise.html
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0.1 KB
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15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx
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14.7 KB
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15.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx
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13.1 KB
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16. Test for the mean. Independent samples (Part 1).mp4
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35.6 MB
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16. Test for the mean. Independent samples (Part 1).srt
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5.6 KB
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16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx
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9.9 KB
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17. Test for the mean. Independent samples (Part 1). Exercise.html
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17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx
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11.0 KB
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17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx
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11.5 KB
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18. Test for the mean. Independent samples (Part 2).mp4
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38.2 MB
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18. Test for the mean. Independent samples (Part 2).srt
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5.3 KB
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18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx
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9.5 KB
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19. Test for the mean. Independent samples (Part 2).html
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0.2 KB
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2. Further Reading on Null and Alternative Hypothesis.html
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2.3 KB
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20. Test for the mean. Independent samples (Part 2) Exercise.html
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0.1 KB
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20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx
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10.8 KB
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20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx
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11.7 KB
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3. Null vs Alternative Hypothesis.html
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0.2 KB
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4. Rejection Region and Significance Level.mp4
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86.6 MB
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4. Rejection Region and Significance Level.srt
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8.9 KB
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4.1 Course notes_hypothesis_testing.pdf.pdf
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663.8 KB
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5. Rejection Region and Significance Level.html
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0.2 KB
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6. Type I Error and Type II Error.mp4
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46.1 MB
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6. Type I Error and Type II Error.srt
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5.8 KB
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7. Type I Error and Type II Error.html
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0.2 KB
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8. Test for the Mean. Population Variance Known.mp4
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56.9 MB
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8. Test for the Mean. Population Variance Known.srt
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8.3 KB
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8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx
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11.2 KB
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9. Test for the Mean. Population Variance Known Exercise.html
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0.1 KB
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9.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx
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11.5 KB
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9.2 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx
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11.3 KB
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/21. Statistics - Practical Example Hypothesis Testing/
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1. Practical Example Hypothesis Testing.mp4
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72.9 MB
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1. Practical Example Hypothesis Testing.srt
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8.7 KB
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1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx
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53.0 KB
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2. Practical Example Hypothesis Testing Exercise.html
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0.1 KB
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2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx
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45.1 KB
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2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx
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44.4 KB
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/22. Part 4 Introduction to Python/
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1. Introduction to Programming.mp4
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61.4 MB
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1. Introduction to Programming.srt
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7.1 KB
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10. Jupyter's Interface.html
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0.2 KB
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11. Python 2 vs Python 3.mp4
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11.8 MB
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11. Python 2 vs Python 3.srt
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3.4 KB
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11.1 Python Introduction - Course Notes.pdf.pdf
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2.1 MB
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2. Introduction to Programming.html
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0.2 KB
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3. Why Python.mp4
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78.7 MB
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3. Why Python.srt
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78.7 MB
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4. Why Python.html
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0.2 KB
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5. Why Jupyter.mp4
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46.5 MB
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5. Why Jupyter.srt
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92.9 MB
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6. Why Jupyter.html
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0.2 KB
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7. Installing Python and Jupyter.mp4
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53.5 MB
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7. Installing Python and Jupyter.srt
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9.1 KB
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8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4
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14.5 MB
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8. Understanding Jupyter's Interface - the Notebook Dashboard.srt
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3.8 KB
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9. Prerequisites for Coding in the Jupyter Notebooks.mp4
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32.1 MB
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/23. Python - Variables and Data Types/
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1. Variables.mp4
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26.5 MB
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1. Variables.srt
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6.2 KB
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1.1 Python Introduction - Course Notes.pdf.pdf
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2.1 MB
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1.2 Variables - Resources.html
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0.1 KB
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2. Variables.html
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0.2 KB
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3. Numbers and Boolean Values in Python.mp4
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17.9 MB
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3. Numbers and Boolean Values in Python.srt
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3.8 KB
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3.1 Numbers and Boolean Values - Resources.html
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0.1 KB
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4. Numbers and Boolean Values in Python.html
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0.2 KB
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5. Python Strings.mp4
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53.1 MB
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5. Python Strings.srt
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14.9 KB
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5.1 Strings - Resources.html
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0.1 KB
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6. Python Strings.html
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/24. Python - Basic Python Syntax/
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1. Using Arithmetic Operators in Python.mp4
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19.8 MB
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1. Using Arithmetic Operators in Python.srt
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4.2 KB
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1.1 Arithmetic Operators - Resources.html
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0.1 KB
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10. Indexing Elements.mp4
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6.2 MB
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10. Indexing Elements.srt
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1.7 KB
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10.1 Indexing Elements - Resources.html
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0.1 KB
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11. Indexing Elements.html
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0.2 KB
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12. Structuring with Indentation.mp4
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13.8 MB
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12. Structuring with Indentation.srt
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4.8 KB
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12.1 Structure Your Code with Indentation - Resources.html
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0.1 KB
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13. Structuring with Indentation.html
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0.2 KB
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2. Using Arithmetic Operators in Python.html
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0.2 KB
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3. The Double Equality Sign.mp4
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6.3 MB
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3. The Double Equality Sign.srt
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1.9 KB
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3.1 The Double Equality Sign - Resources.html
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0.1 KB
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4. The Double Equality Sign.html
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0.2 KB
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5. How to Reassign Values.mp4
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4.2 MB
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5. How to Reassign Values.srt
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1.3 KB
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5.1 Reassign Values - Resources.html
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0.1 KB
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6. How to Reassign Values.html
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0.2 KB
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7. Add Comments.mp4
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11.8 MB
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7. Add Comments.srt
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4.0 KB
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7.1 Add Comments - Resources.html
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0.1 KB
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8. Add Comments.html
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0.2 KB
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9. Understanding Line Continuation.mp4
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2.5 MB
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1.2 KB
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9.1 Line Continuation - Resources.html
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/25. Python - Other Python Operators/
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1. Comparison Operators.mp4
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10.7 MB
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1. Comparison Operators.srt
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2.5 KB
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1.1 Comparison Operators - Resources.html
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0.1 KB
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2. Comparison Operators.html
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0.2 KB
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3. Logical and Identity Operators.mp4
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31.5 MB
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3. Logical and Identity Operators.srt
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5.9 KB
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3.1 Logical and Identity Operators - Resources.html
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0.1 KB
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4. Logical and Identity Operators.html
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0.2 KB
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/26. Python - Conditional Statements/
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1. The IF Statement.mp4
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24.4 MB
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1. The IF Statement.srt
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7.8 KB
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1.1 Introduction to the If Statement - Resources.html
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0.1 KB
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2. The IF Statement.html
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0.2 KB
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3. The ELSE Statement.mp4
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24.4 MB
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3. The ELSE Statement.srt
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6.4 KB
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3.1 Add an Else Statement - Resources.html
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0.1 KB
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4. The ELIF Statement.mp4
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55.9 MB
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4. The ELIF Statement.srt
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55.9 MB
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4.1 Else if, for Brief - Elif - Resources.html
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0.1 KB
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5. A Note on Boolean Values.mp4
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21.0 MB
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5. A Note on Boolean Values.srt
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5.1 A Note on Boolean Values - Resources.html
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0.1 KB
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6. A Note on Boolean Values.html
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/27. Python - Python Functions/
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1. Defining a Function in Python.mp4
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15.5 MB
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1. Defining a Function in Python.srt
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5.4 KB
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1.1 Defining a Function in Python - Resources.html
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0.1 KB
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2. How to Create a Function with a Parameter.mp4
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40.0 MB
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2. How to Create a Function with a Parameter.srt
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9.2 KB
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2.1 Creating a Function with a Parameter - Resources.html
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0.1 KB
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3. Defining a Function in Python - Part II.mp4
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26.5 MB
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3. Defining a Function in Python - Part II.srt
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26.5 MB
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3.1 Another Way to Define a Function - Resources.html
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0.1 KB
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4. How to Use a Function within a Function.mp4
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8.5 MB
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4. How to Use a Function within a Function.srt
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2.1 KB
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4.1 Using a Function in Another Function - Resources.html
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0.1 KB
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5. Conditional Statements and Functions.mp4
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16.4 MB
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5. Conditional Statements and Functions.srt
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3.6 KB
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5.1 Combining Conditional Statements and Functions - Resources.html
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0.1 KB
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6. Functions Containing a Few Arguments.mp4
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15.4 MB
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6. Functions Containing a Few Arguments.srt
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3.1 KB
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6.1 Creating Functions Containing a Few Arguments - Resources.html
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0.1 KB
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7. Built-in Functions in Python.mp4
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23.1 MB
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7. Built-in Functions in Python.srt
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4.3 KB
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7.1 Notable Built-In Functions in Python - Resources.html
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8. Python Functions.html
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/28. Python - Sequences/
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1. Lists.mp4
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39.6 MB
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1. Lists.srt
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10.1 KB
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1.1 Lists - Resources.html
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0.1 KB
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2. Lists.html
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0.2 KB
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3. Using Methods.mp4
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39.4 MB
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3. Using Methods.srt
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8.6 KB
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3.1 Help Yourself with Methods - Resources.html
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0.1 KB
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4. Using Methods.html
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0.2 KB
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5. List Slicing.mp4
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32.3 MB
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5. List Slicing.srt
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5.7 KB
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5.1 List Slicing - Resources.html
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0.1 KB
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6. Tuples.mp4
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30.9 MB
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6. Tuples.srt
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7.1 KB
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6.1 Tuples - Resources.html
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0.1 KB
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7. Dictionaries.mp4
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43.7 MB
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7. Dictionaries.srt
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8.6 KB
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7.1 Dictionaries - Resources.html
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8. Dictionaries.html
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/29. Python - Iterations/
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1. For Loops.mp4
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24.7 MB
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1. For Loops.srt
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6.7 KB
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1.1 For Loops - Resources.html
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0.1 KB
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2. For Loops.html
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0.2 KB
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3. While Loops and Incrementing.mp4
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29.8 MB
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3. While Loops and Incrementing.srt
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6.0 KB
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3.1 While Loops and Incrementing - Resources.html
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0.1 KB
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4. Lists with the range() Function.mp4
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27.0 MB
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4. Lists with the range() Function.srt
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7.8 KB
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4.1 Create Lists with the range() Function - Resources.html
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0.1 KB
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5. Lists with the range() Function.html
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0.2 KB
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6. Conditional Statements and Loops.mp4
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29.1 MB
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6. Conditional Statements and Loops.srt
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7.6 KB
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6.1 Use Conditional Statements and Loops Together - Resources.html
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0.1 KB
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7. Conditional Statements, Functions, and Loops.mp4
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9.9 MB
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7. Conditional Statements, Functions, and Loops.srt
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2.5 KB
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7.1 All In - Conditional Statements, Functions, and Loops - Resources.html
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0.1 KB
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8. How to Iterate over Dictionaries.mp4
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31.1 MB
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8. How to Iterate over Dictionaries.srt
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8.1 Iterating over Dictionaries - Resources.html
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0.1 KB
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/3. The Field of Data Science - Connecting the Data Science Disciplines/
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1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4
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133.0 MB
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1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt
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9.2 KB
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2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html
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/30. Python - Advanced Python Tools/
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1. Object Oriented Programming.mp4
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35.2 MB
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1. Object Oriented Programming.srt
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6.2 KB
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2. Object Oriented Programming.html
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0.2 KB
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3. Modules and Packages.mp4
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8.9 MB
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3. Modules and Packages.srt
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1.3 KB
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4. Modules and Packages.html
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0.2 KB
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5. What is the Standard Library.mp4
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18.9 MB
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5. What is the Standard Library.srt
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3.6 KB
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6. What is the Standard Library.html
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0.2 KB
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7. Importing Modules in Python.mp4
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20.9 MB
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7. Importing Modules in Python.srt
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4.9 KB
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8. Importing Modules in Python.html
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0.2 KB
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/31. Part 5 Advanced Statistical Methods in Python/
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1. Introduction to Regression Analysis.mp4
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18.2 MB
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1. Introduction to Regression Analysis.srt
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2.3 KB
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2. Introduction to Regression Analysis.html
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0.2 KB
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/32. Advanced Statistical Methods - Linear regression with StatsModels/
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1. The Linear Regression Model.mp4
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60.2 MB
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1. The Linear Regression Model.srt
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7.2 KB
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10. Using Seaborn for Graphs.mp4
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12.8 MB
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10. Using Seaborn for Graphs.srt
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1.5 KB
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11. How to Interpret the Regression Table.mp4
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46.8 MB
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11. How to Interpret the Regression Table.srt
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6.5 KB
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12. How to Interpret the Regression Table.html
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0.2 KB
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13. Decomposition of Variability.mp4
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52.1 MB
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13. Decomposition of Variability.srt
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4.3 KB
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14. Decomposition of Variability.html
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0.2 KB
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15. What is the OLS.mp4
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29.7 MB
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15. What is the OLS.srt
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3.9 KB
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16. What is the OLS.html
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0.2 KB
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17. R-Squared.mp4
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43.0 MB
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17. R-Squared.srt
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6.7 KB
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18. R-Squared.html
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0.2 KB
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2. The Linear Regression Model.html
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0.2 KB
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3. Correlation vs Regression.mp4
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15.4 MB
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3. Correlation vs Regression.srt
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2.1 KB
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4. Correlation vs Regression.html
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0.2 KB
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5. Geometrical Representation of the Linear Regression Model.mp4
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5.4 MB
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5. Geometrical Representation of the Linear Regression Model.srt
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1.7 KB
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6. Geometrical Representation of the Linear Regression Model.html
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0.2 KB
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7. Python Packages Installation.mp4
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42.6 MB
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7. Python Packages Installation.srt
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20.1 MB
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8. First Regression in Python.mp4
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46.7 MB
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8. First Regression in Python.srt
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8.1 KB
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8.1 First regression in Python.html
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0.1 KB
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9. First Regression in Python Exercise.html
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1.4 KB
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9.1 First regression in Python - Exercise.html
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0.1 KB
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/33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/
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1. Multiple Linear Regression.mp4
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22.6 MB
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1. Multiple Linear Regression.srt
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3.4 KB
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10. A1 Linearity.html
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0.2 KB
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11. A2 No Endogeneity.mp4
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37.4 MB
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11. A2 No Endogeneity.srt
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5.4 KB
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12. A2 No Endogeneity.html
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0.2 KB
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13. A3 Normality and Homoscedasticity.mp4
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44.8 MB
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13. A3 Normality and Homoscedasticity.srt
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6.8 KB
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14. A4 No Autocorrelation.mp4
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33.0 MB
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14. A4 No Autocorrelation.srt
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5.0 KB
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15. A4 No autocorrelation.html
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0.2 KB
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16. A5 No Multicollinearity.mp4
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30.1 MB
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16. A5 No Multicollinearity.srt
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4.7 KB
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17. A5 No Multicollinearity.html
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0.2 KB
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18. Dealing with Categorical Data - Dummy Variables.mp4
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58.4 MB
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18. Dealing with Categorical Data - Dummy Variables.srt
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8.3 KB
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18.1 Dealing with categorical data.html
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0.1 KB
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19. Dealing with Categorical Data - Dummy Variables.html
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19.1 Dealing with categorical data.html
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2. Multiple Linear Regression.html
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20. Making Predictions with the Linear Regression.mp4
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25.9 MB
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20. Making Predictions with the Linear Regression.srt
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20.1 Making predictions.html
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3. Adjusted R-Squared.mp4
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57.5 MB
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3. Adjusted R-Squared.srt
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3.1 Adjusted R-squared.html
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5. Multiple Linear Regression Exercise.html
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6. Test for Significance of the Model (F-Test).mp4
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7. OLS Assumptions.mp4
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7. OLS Assumptions.srt
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8. OLS Assumptions.html
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9. A1 Linearity.mp4
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/34. Advanced Statistical Methods - Linear Regression with sklearn/
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1. What is sklearn and How is it Different from Other Packages.mp4
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28.6 MB
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1. What is sklearn and How is it Different from Other Packages.srt
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3.5 KB
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10. Feature Selection (F-regression).mp4
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31.0 MB
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10. Feature Selection (F-regression).srt
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31.0 MB
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10.1 Feature selection.html
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0.1 KB
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11. A Note on Calculation of P-values with sklearn.html
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0.4 KB
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11.1 Calculation of P-values.html
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0.1 KB
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12. Creating a Summary Table with p-values.mp4
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12.9 MB
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12. Creating a Summary Table with p-values.srt
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12.9 MB
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12.1 Summary table with p-values.html
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13. Multiple Linear Regression - Exercise.html
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13.1 Multiple linear regression - Exercise.html
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14. Feature Scaling (Standardization).mp4
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41.0 MB
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14. Feature Scaling (Standardization).srt
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7.5 MB
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14.1 Feature scaling.html
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0.1 KB
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15. Feature Selection through Standardization of Weights.mp4
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36.6 MB
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15. Feature Selection through Standardization of Weights.srt
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15.1 Feature scaling standardization.html
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16. Predicting with the Standardized Coefficients.mp4
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27.2 MB
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16. Predicting with the Standardized Coefficients.srt
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16.1 Predicting with the Standardized Cofficients.html
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17. Feature Scaling (Standardization) - Exercise.html
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17.1 Feature scaling - exercise.html
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18. Underfitting and Overfitting.mp4
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17.8 MB
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18. Underfitting and Overfitting.srt
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3.5 KB
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19. Train - Test Split Explained.mp4
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51.6 MB
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19. Train - Test Split Explained.srt
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19.1 Train - Test split explained.html
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2. How are Going to Approach this Section.mp4
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2. How are Going to Approach this Section.srt
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3. Simple Linear Regression with sklearn.mp4
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36.5 MB
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3.1 Simple Linear Regression with sklearn with Comments.html
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3.2 Simple Linear Regression with sklearn.html
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3.3 1.01. Simple linear regression.csv.csv
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4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4
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33.6 MB
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4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt
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4.1 Simple Linear Regression with sklearn.html
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4.2 1.01. Simple linear regression.csv.csv
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4.3 Simple Linear Regression with sklearn with Comments.html
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0.2 KB
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5. A Note on Normalization.html
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6. Simple Linear Regression with sklearn - Exercise.html
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6.1 Simple linear regression with sklearn.html
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7. Multiple Linear Regression with sklearn.mp4
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21.0 MB
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7.1 Multiple Linear Regression with sklearn.html
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7.2 1.02. Multiple linear regression.csv.csv
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7.3 Multiple Linear Regression with sklearn with Comments.html
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8. Calculating the Adjusted R-Squared in sklearn.mp4
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32.4 MB
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8.3 1.02. Multiple linear regression.csv.csv
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9.1 Calculating the Adjusted R-Squared.html
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/35. Advanced Statistical Methods - Practical Example Linear Regression/
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1. Practical Example Linear Regression (Part 1).mp4
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101.8 MB
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1. Practical Example Linear Regression (Part 1).srt
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15.2 KB
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1.1 sklearn - Linear Regression - Practical Example (Part 1).html
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2. Practical Example Linear Regression (Part 2).mp4
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48.2 MB
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2. Practical Example Linear Regression (Part 2).srt
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8.2 KB
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2.1 sklearn - Linear Regression - Practical Example (Part 2).html
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0.1 KB
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3. A Note on Multicollinearity.html
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0.8 KB
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4. Practical Example Linear Regression (Part 3).mp4
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24.8 MB
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4. Practical Example Linear Regression (Part 3).srt
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4.2 KB
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4.1 sklearn - Linear Regression - Practical Example (Part 3).html
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5. Dummies and Variance Inflation Factor - Exercise.html
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5.1 Dummies and VIF - Exercise and Solution.html
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0.1 KB
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6. Practical Example Linear Regression (Part 4).mp4
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58.8 MB
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11.8 KB
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6.1 sklearn - Linear Regression - Practical Example (Part 4).html
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7. Dummy Variables - Exercise.html
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8. Practical Example Linear Regression (Part 5).mp4
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60.7 MB
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8. Practical Example Linear Regression (Part 5).srt
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8.1 sklearn - Linear Regression - Practical Example (Part 5).html
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/36. Advanced Statistical Methods - Logistic Regression/
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1. Introduction to Logistic Regression.mp4
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28.4 MB
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1. Introduction to Logistic Regression.srt
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1.7 KB
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10. Binary Predictors in a Logistic Regression.mp4
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40.3 MB
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10. Binary Predictors in a Logistic Regression.srt
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5.5 KB
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10.1 Binary predictors.html
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0.1 KB
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11. Binary Predictors in a Logistic Regression - Exercise.html
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11.1 Binary predictors - exercise.html
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0.1 KB
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11.2 Bank_data.csv.csv
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20.0 KB
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12. Calculating the Accuracy of the Model.mp4
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34.5 MB
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12. Calculating the Accuracy of the Model.srt
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12.1 Accuracy.html
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13. Calculating the Accuracy of the Model.html
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13.1 Bank_data.csv.csv
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20.0 KB
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14. Underfitting and Overfitting.mp4
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23.4 MB
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14. Underfitting and Overfitting.srt
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5.1 KB
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15. Testing the Model.mp4
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33.8 MB
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15. Testing the Model.srt
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15.1 Testing the model.html
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16. Testing the Model - Exercise.html
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16.1 Testing the model - exercise.html
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0.1 KB
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16.2 Bank_data.csv.csv
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20.0 KB
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8.5 KB
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2. A Simple Example in Python.mp4
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36.4 MB
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2. A Simple Example in Python.srt
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5.9 KB
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2.1 A simple example in Python.html
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0.1 KB
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3. Logistic vs Logit Function.mp4
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90.7 MB
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3. Logistic vs Logit Function.srt
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5.0 KB
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4. Building a Logistic Regression.mp4
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17.9 MB
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4. Building a Logistic Regression.srt
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3.4 KB
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4.1 Building a logistic regression.html
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0.1 KB
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5. Building a Logistic Regression - Exercise.html
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0.1 KB
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5.1 Example_bank_data.csv.csv
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6.4 KB
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5.2 Building a logistic regression.html
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0.1 KB
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6. An Invaluable Coding Tip.mp4
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24.2 MB
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6. An Invaluable Coding Tip.srt
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3.3 KB
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7. Understanding Logistic Regression Tables.mp4
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32.0 MB
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5.7 KB
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8. Understanding Logistic Regression Tables - Exercise.html
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0.1 KB
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8.1 Understanding logistic regression.html
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0.1 KB
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8.2 Bank_data.csv.csv
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20.0 KB
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9. What do the Odds Actually Mean.mp4
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33.8 MB
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4.9 KB
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/37. Advanced Statistical Methods - Cluster Analysis/
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1. Introduction to Cluster Analysis.mp4
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56.0 MB
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1. Introduction to Cluster Analysis.srt
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4.9 KB
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2. Some Examples of Clusters.mp4
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75.0 MB
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2. Some Examples of Clusters.srt
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6.4 KB
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3. Difference between Classification and Clustering.mp4
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37.9 MB
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3. Difference between Classification and Clustering.srt
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3.4 KB
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4. Math Prerequisites.mp4
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15.3 MB
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4. Math Prerequisites.srt
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4.2 KB
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/38. Advanced Statistical Methods - K-Means Clustering/
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1. K-Means Clustering.mp4
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28.6 MB
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1. K-Means Clustering.srt
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6.8 KB
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10. Relationship between Clustering and Regression.mp4
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10.4 MB
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10. Relationship between Clustering and Regression.srt
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2.2 KB
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11. Market Segmentation with Cluster Analysis (Part 1).mp4
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45.1 MB
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11. Market Segmentation with Cluster Analysis (Part 1).srt
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7.7 KB
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11.1 Market segmentation.html
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0.1 KB
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12. Market Segmentation with Cluster Analysis (Part 2).mp4
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58.8 MB
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12. Market Segmentation with Cluster Analysis (Part 2).srt
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9.4 KB
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12.1 Market segmentation.html
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0.1 KB
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13. How is Clustering Useful.mp4
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78.1 MB
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13. How is Clustering Useful.srt
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14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html
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0.1 KB
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14.1 iris_dataset.csv.csv
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14.2 Exercise - part 1.html
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15.1 iris_dataset.csv.csv
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15.3 Exercise - part 2.html
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0.1 KB
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2. A Simple Example of Clustering.mp4
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54.3 MB
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2. A Simple Example of Clustering.srt
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9.8 KB
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2.1 Example of clustering.html
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0.1 KB
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3. A Simple Example of Clustering - Exercise.html
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0.1 KB
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3.1 A simple example of clustering.html
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0.1 KB
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3.2 Countries_exercise.csv.csv
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4. Clustering Categorical Data.mp4
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22.3 MB
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4. Clustering Categorical Data.srt
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3.3 KB
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4.1 Clustering categorical data.html
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0.1 KB
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5. Clustering Categorical Data - Exercise.html
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0.1 KB
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5.1 Categorical.csv.csv
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10.6 KB
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5.2 Clustering categorical data.html
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0.1 KB
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6. How to Choose the Number of Clusters.mp4
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46.3 MB
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6. How to Choose the Number of Clusters.srt
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7.5 KB
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6.1 How to choose the number of clusters.html
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0.1 KB
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7. How to Choose the Number of Clusters - Exercise.html
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7.1 Countries_exercise.csv.csv
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8.5 KB
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0.1 KB
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8. Pros and Cons of K-Means Clustering.mp4
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39.5 MB
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8. Pros and Cons of K-Means Clustering.srt
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4.7 KB
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9. To Standardize or not to Standardize.mp4
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31.6 MB
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6.0 KB
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/39. Advanced Statistical Methods - Other Types of Clustering/
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1. Types of Clustering.mp4
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46.7 MB
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1. Types of Clustering.srt
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4.8 KB
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2. Dendrogram.mp4
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30.5 MB
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2. Dendrogram.srt
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7.5 KB
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3. Heatmaps.mp4
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31.1 MB
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3. Heatmaps.srt
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6.5 KB
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3.1 Heatmaps.html
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0.1 KB
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/4. The Field of Data Science - The Benefits of Each Discipline/
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1. The Reason behind these Disciplines.mp4
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85.1 MB
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1. The Reason behind these Disciplines.srt
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6.7 KB
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2. The Reason behind these Disciplines.html
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0.2 KB
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/40. Part 6 Mathematics/
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1. What is a matrix.mp4
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35.2 MB
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1. What is a matrix.srt
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4.4 KB
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10. Addition and Subtraction of Matrices.mp4
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34.2 MB
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10. Addition and Subtraction of Matrices.srt
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4.1 KB
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10.1 Addition and Subtraction of Matrices Python Notebook.html
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0.2 KB
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11. Addition and Subtraction of Matrices.html
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0.2 KB
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12. Errors when Adding Matrices.mp4
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11.7 MB
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12. Errors when Adding Matrices.srt
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2.6 KB
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12.1 Errors when Adding Matrices Python Notebook.html
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0.2 KB
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13. Transpose of a Matrix.mp4
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39.9 MB
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13. Transpose of a Matrix.srt
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5.5 KB
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13.1 Transpose of a Matrix Python Notebook.html
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0.2 KB
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14. Dot Product.mp4
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25.2 MB
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14. Dot Product.srt
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4.4 KB
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14.1 Dot Product Python Notebook.html
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0.2 KB
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15. Dot Product of Matrices.mp4
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51.8 MB
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15. Dot Product of Matrices.srt
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9.7 KB
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15.1 Dot Product of Matrices Python Notebook.html
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0.2 KB
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16. Why is Linear Algebra Useful.mp4
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151.3 MB
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16. Why is Linear Algebra Useful.srt
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12.1 KB
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2. What is a Matrix.html
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0.2 KB
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3. Scalars and Vectors.mp4
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35.5 MB
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3. Scalars and Vectors.srt
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3.9 KB
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4. Scalars and Vectors.html
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0.2 KB
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5. Linear Algebra and Geometry.mp4
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52.2 MB
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5. Linear Algebra and Geometry.srt
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4.2 KB
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6. Linear Algebra and Geometry.html
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0.2 KB
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7. Arrays in Python - A Convenient Way To Represent Matrices.mp4
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28.0 MB
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7. Arrays in Python - A Convenient Way To Represent Matrices.srt
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6.3 KB
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7.1 Arrays in Python Notebook.html
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0.2 KB
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8. What is a Tensor.mp4
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23.6 MB
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8. What is a Tensor.srt
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3.7 KB
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8.1 Tensors Notebook.html
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0.1 KB
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/41. Part 7 Deep Learning/
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1. What to Expect from this Part.mp4
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32.6 MB
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1. What to Expect from this Part.srt
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4.7 KB
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2. What is Machine Learning.html
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0.2 KB
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/42. Deep Learning - Introduction to Neural Networks/
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1. Introduction to Neural Networks.mp4
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45.0 MB
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1. Introduction to Neural Networks.srt
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6.0 KB
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1.1 Course Notes - Section 2.pdf.pdf
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592.0 KB
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10. The Linear Model with Multiple Inputs.html
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0.2 KB
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11. The Linear model with Multiple Inputs and Multiple Outputs.mp4
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40.2 MB
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11. The Linear model with Multiple Inputs and Multiple Outputs.srt
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5.6 KB
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12. The Linear model with Multiple Inputs and Multiple Outputs.html
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0.2 KB
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13. Graphical Representation of Simple Neural Networks.mp4
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23.7 MB
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13. Graphical Representation of Simple Neural Networks.srt
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2.8 KB
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14. Graphical Representation of Simple Neural Networks.html
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0.2 KB
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15. What is the Objective Function.mp4
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18.8 MB
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15. What is the Objective Function.srt
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2.2 KB
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16. What is the Objective Function.html
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0.2 KB
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17. Common Objective Functions L2-norm Loss.mp4
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24.4 MB
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17. Common Objective Functions L2-norm Loss.srt
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2.8 KB
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18. Common Objective Functions L2-norm Loss.html
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0.2 KB
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19. Common Objective Functions Cross-Entropy Loss.mp4
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39.1 MB
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19. Common Objective Functions Cross-Entropy Loss.srt
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5.4 KB
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2. Introduction to Neural Networks.html
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0.2 KB
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20. Common Objective Functions Cross-Entropy Loss.html
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0.2 KB
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21. Optimization Algorithm 1-Parameter Gradient Descent.mp4
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58.3 MB
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21. Optimization Algorithm 1-Parameter Gradient Descent.srt
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8.7 KB
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21.1 GD-function-example.xlsx.xlsx
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43.4 KB
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22. Optimization Algorithm 1-Parameter Gradient Descent.html
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0.2 KB
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23. Optimization Algorithm n-Parameter Gradient Descent.mp4
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41.3 MB
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23. Optimization Algorithm n-Parameter Gradient Descent.srt
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7.7 KB
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24. Optimization Algorithm n-Parameter Gradient Descent.html
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0.2 KB
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3. Training the Model.mp4
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30.1 MB
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3. Training the Model.srt
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4.4 KB
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3.1 Course Notes - Section 2.pdf.pdf
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592.0 KB
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4. Training the Model.html
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0.2 KB
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5. Types of Machine Learning.mp4
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47.3 MB
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5. Types of Machine Learning.srt
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5.4 KB
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6. Types of Machine Learning.html
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0.2 KB
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7. The Linear Model (Linear Algebraic Version).mp4
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29.8 MB
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7. The Linear Model (Linear Algebraic Version).srt
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4.0 KB
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8. The Linear Model.html
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0.2 KB
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9. The Linear Model with Multiple Inputs.mp4
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26.3 MB
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3.2 KB
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/43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/
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1. Basic NN Example (Part 1).mp4
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21.6 MB
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1. Basic NN Example (Part 1).srt
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4.6 KB
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1.1 Bais NN Example Part 1.html
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0.1 KB
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1.2 Shortcuts-for-Jupyter.pdf.pdf
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634.0 KB
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2. Basic NN Example (Part 2).mp4
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36.6 MB
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2. Basic NN Example (Part 2).srt
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7.0 KB
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2.1 Basic NN Example (Part 2).html
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0.1 KB
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3. Basic NN Example (Part 3).mp4
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25.6 MB
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3. Basic NN Example (Part 3).srt
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4.6 KB
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3.1 Basic NN Example (Part 3).html
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0.1 KB
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4. Basic NN Example (Part 4).mp4
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64.1 MB
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4. Basic NN Example (Part 4).srt
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11.1 KB
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4.1 Basic NN Example (Part 4).html
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0.1 KB
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5. Basic NN Example Exercises.html
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1.7 KB
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5.1 Basic NN Example Exercise 3b Solution.html
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0.2 KB
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5.10 Basic NN Example Exercise 5 Solution.html
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0.1 KB
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5.2 Basic NN Example Exercise 1 Solution.html
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0.1 KB
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5.3 Basic NN Example Exercise 3a Solution.html
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0.2 KB
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5.4 Basic NN Example Exercise 3d Solution.html
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0.2 KB
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5.5 Basic NN Example Exercise 3c Solution.html
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0.2 KB
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5.6 Basic NN Example (All Exercises).html
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0.1 KB
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5.7 Basic NN Example Exercise 4 Solution.html
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0.1 KB
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5.8 Basic NN Example Exercise 6 Solution.html
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0.1 KB
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5.9 Basic NN Example Exercise 2 Solution.html
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0.1 KB
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/44. Deep Learning - TensorFlow 2.0 Introduction/
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1. How to Install TensorFlow 2.0.mp4
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40.6 MB
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1. How to Install TensorFlow 2.0.srt
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6.5 KB
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1.1 Shortcuts-for-Jupyter.pdf.pdf
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634.0 KB
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2. TensorFlow Outline and Comparison with Other Libraries.mp4
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35.1 MB
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2. TensorFlow Outline and Comparison with Other Libraries.srt
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5.4 KB
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3. TensorFlow 1 vs TensorFlow 2.mp4
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23.1 MB
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3. TensorFlow 1 vs TensorFlow 2.srt
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3.7 KB
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4. A Note on TensorFlow 2 Syntax.mp4
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7.1 MB
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4. A Note on TensorFlow 2 Syntax.srt
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1.4 KB
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4.1 A note on TensorFlow 2 Syntax.html
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0.1 KB
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5. Types of File Formats Supporting TensorFlow.mp4
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17.2 MB
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5. Types of File Formats Supporting TensorFlow.srt
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3.6 KB
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5.1 Types of File Formats.html
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0.1 KB
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6. Outlining the Model with TensorFlow 2.mp4
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36.4 MB
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6. Outlining the Model with TensorFlow 2.srt
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8.0 KB
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6.1 Outlining the Model.html
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0.1 KB
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7. Interpreting the Result and Extracting the Weights and Bias.mp4
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31.7 MB
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7. Interpreting the Result and Extracting the Weights and Bias.srt
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6.4 KB
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7.1 Interpreting the Result.html
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0.1 KB
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8. Customizing a TensorFlow 2 Model.mp4
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24.0 MB
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8. Customizing a TensorFlow 2 Model.srt
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4.2 KB
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8.1 Customizing a TensorFlow 2 Model.html
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0.1 KB
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9. Basic NN with TensorFlow Exercises.html
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1.3 KB
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9.1 Basic NN with TensorFlow.html
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0.1 KB
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/45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/
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1. What is a Layer.mp4
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13.1 MB
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1. What is a Layer.srt
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2.4 KB
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1.1 Course Notes - Section 6.pdf.pdf
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958.9 KB
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2. What is a Deep Net.mp4
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31.0 MB
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2. What is a Deep Net.srt
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3.3 KB
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2.1 Course Notes - Section 6.pdf.pdf
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958.9 KB
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3. Digging into a Deep Net.mp4
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62.2 MB
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3. Digging into a Deep Net.srt
|
6.9 KB
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4. Non-Linearities and their Purpose.mp4
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29.0 MB
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4. Non-Linearities and their Purpose.srt
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4.0 KB
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5. Activation Functions.mp4
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26.3 MB
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5. Activation Functions.srt
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5.4 KB
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6. Activation Functions Softmax Activation.mp4
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27.2 MB
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6. Activation Functions Softmax Activation.srt
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4.6 KB
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7. Backpropagation.mp4
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36.6 MB
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7. Backpropagation.srt
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4.6 KB
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8. Backpropagation picture.mp4
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20.4 MB
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8. Backpropagation picture.srt
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4.1 KB
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9. Backpropagation - A Peek into the Mathematics of Optimization.html
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0.5 KB
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9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf
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186.7 KB
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/46. Deep Learning - Overfitting/
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1. What is Overfitting.mp4
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32.6 MB
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1. What is Overfitting.srt
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5.7 KB
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2. Underfitting and Overfitting for Classification.mp4
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26.3 MB
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2. Underfitting and Overfitting for Classification.srt
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2.7 KB
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3. What is Validation.mp4
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34.3 MB
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3. What is Validation.srt
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5.0 KB
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4. Training, Validation, and Test Datasets.mp4
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26.4 MB
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4. Training, Validation, and Test Datasets.srt
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3.7 KB
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5. N-Fold Cross Validation.mp4
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21.7 MB
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5. N-Fold Cross Validation.srt
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4.3 KB
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6. Early Stopping or When to Stop Training.mp4
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25.3 MB
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6. Early Stopping or When to Stop Training.srt
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7.0 KB
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/47. Deep Learning - Initialization/
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1. What is Initialization.mp4
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22.8 MB
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1. What is Initialization.srt
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3.6 KB
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2. Types of Simple Initializations.mp4
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15.0 MB
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2. Types of Simple Initializations.srt
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3.8 KB
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3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4
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18.0 MB
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3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt
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3.8 KB
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/48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/
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1. Stochastic Gradient Descent.mp4
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30.1 MB
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1. Stochastic Gradient Descent.srt
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4.9 KB
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2. Problems with Gradient Descent.mp4
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11.5 MB
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2. Problems with Gradient Descent.srt
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2.9 KB
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3. Momentum.mp4
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17.2 MB
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3. Momentum.srt
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3.5 KB
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4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4
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30.5 MB
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4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt
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6.1 KB
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5. Learning Rate Schedules Visualized.mp4
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9.6 MB
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5. Learning Rate Schedules Visualized.srt
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2.2 KB
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6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4
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27.6 MB
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6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt
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5.3 KB
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7. Adam (Adaptive Moment Estimation).mp4
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23.4 MB
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7. Adam (Adaptive Moment Estimation).srt
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3.4 KB
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/49. Deep Learning - Preprocessing/
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1. Preprocessing Introduction.mp4
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29.1 MB
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1. Preprocessing Introduction.srt
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4.0 KB
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2. Types of Basic Preprocessing.mp4
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12.4 MB
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2. Types of Basic Preprocessing.srt
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1.7 KB
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3. Standardization.mp4
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53.5 MB
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3. Standardization.srt
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6.1 KB
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4. Preprocessing Categorical Data.mp4
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19.5 MB
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4. Preprocessing Categorical Data.srt
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2.8 KB
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5. Binary and One-Hot Encoding.mp4
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30.4 MB
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5. Binary and One-Hot Encoding.srt
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4.9 KB
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/5. The Field of Data Science - Popular Data Science Techniques/
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1. Techniques for Working with Traditional Data.mp4
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145.0 MB
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1. Techniques for Working with Traditional Data.srt
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10.9 KB
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10. Techniques for Working with Traditional Methods.mp4
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117.1 MB
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10. Techniques for Working with Traditional Methods.srt
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11.2 KB
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11. Techniques for Working with Traditional Methods.html
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0.2 KB
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12. Real Life Examples of Traditional Methods.mp4
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44.9 MB
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12. Real Life Examples of Traditional Methods.srt
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3.7 KB
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13. Machine Learning (ML) Techniques.mp4
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104.1 MB
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13. Machine Learning (ML) Techniques.srt
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8.9 KB
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14. Machine Learning (ML) Techniques.html
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0.2 KB
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15. Types of Machine Learning.mp4
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131.2 MB
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15. Types of Machine Learning.srt
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10.8 KB
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16. Types of Machine Learning.html
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0.2 KB
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17. Real Life Examples of Machine Learning (ML).mp4
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38.6 MB
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17. Real Life Examples of Machine Learning (ML).srt
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3.0 KB
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18. Real Life Examples of Machine Learning (ML).html
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0.2 KB
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2. Techniques for Working with Traditional Data.html
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0.2 KB
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3. Real Life Examples of Traditional Data.mp4
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31.4 MB
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3. Real Life Examples of Traditional Data.srt
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2.3 KB
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4. Techniques for Working with Big Data.mp4
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79.2 MB
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4. Techniques for Working with Big Data.srt
|
5.8 KB
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5. Techniques for Working with Big Data.html
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0.2 KB
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6. Real Life Examples of Big Data.mp4
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23.1 MB
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6. Real Life Examples of Big Data.srt
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1.9 KB
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7. Business Intelligence (BI) Techniques.mp4
|
94.3 MB
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7. Business Intelligence (BI) Techniques.srt
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8.8 KB
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8. Business Intelligence (BI) Techniques.html
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0.2 KB
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9. Real Life Examples of Business Intelligence (BI).mp4
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31.0 MB
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9. Real Life Examples of Business Intelligence (BI).srt
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2.2 KB
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/50. Deep Learning - Classifying on the MNIST Dataset/
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1. MNIST The Dataset.mp4
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14.0 MB
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1. MNIST The Dataset.srt
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3.7 KB
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10. MNIST Learning.mp4
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43.0 MB
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10. MNIST Learning.srt
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8.1 KB
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10.1 MNIST Learning.html
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0.1 KB
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11. MNIST - Exercises.html
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2.0 KB
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11.1 MNIST - Exercises.html
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0.1 KB
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12. MNIST Testing the Model.mp4
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31.0 MB
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12. MNIST Testing the Model.srt
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6.2 KB
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12.1 MNIST Testing the Model.html
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0.1 KB
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2. MNIST How to Tackle the MNIST.mp4
|
19.6 MB
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2. MNIST How to Tackle the MNIST.srt
|
3.6 KB
|
3. MNIST Importing the Relevant Packages and Loading the Data.mp4
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17.1 MB
|
3. MNIST Importing the Relevant Packages and Loading the Data.srt
|
3.1 KB
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3.1 MNIST Importing the Relevant Packages.html
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0.1 KB
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4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4
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30.5 MB
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4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt
|
6.4 KB
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5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html
|
0.1 KB
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5.1 MNIST Preprocess the Data.html
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0.1 KB
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6. MNIST Preprocess the Data - Shuffle and Batch.mp4
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43.5 MB
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6. MNIST Preprocess the Data - Shuffle and Batch.srt
|
9.5 KB
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7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html
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0.1 KB
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7.1 MNIST Preprocess the Data.html
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0.1 KB
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8. MNIST Outline the Model.mp4
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29.6 MB
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8. MNIST Outline the Model.srt
|
7.4 KB
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8.1 MNIST Outline the Model.html
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0.1 KB
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9. MNIST Select the Loss and the Optimizer.mp4
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14.6 MB
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9. MNIST Select the Loss and the Optimizer.srt
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3.1 KB
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9.1 MNIST Select the Loss and the Optimizer.html
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0.1 KB
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/51. Deep Learning - Business Case Example/
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1. Business Case Exploring the Dataset and Identifying Predictors.mp4
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69.5 MB
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1. Business Case Exploring the Dataset and Identifying Predictors.srt
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10.9 KB
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1.1 Audiobooks_data.csv.csv
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727.8 KB
|
1.2 Business Case Exploring the Dataset.html
|
0.1 KB
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10. Setting an Early Stopping Mechanism - Exercise.html
|
0.2 KB
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11. Business Case Testing the Model.mp4
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11.3 MB
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11. Business Case Testing the Model.srt
|
2.1 KB
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11.1 Business Case Testing the Model.html
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0.1 KB
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12. Business Case Final Exercise.html
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0.4 KB
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12.1 Business Case Final Exercise.html
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0.1 KB
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2. Business Case Outlining the Solution.mp4
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7.7 MB
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2. Business Case Outlining the Solution.srt
|
2.0 KB
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3. Business Case Balancing the Dataset.mp4
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31.9 MB
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3. Business Case Balancing the Dataset.srt
|
4.6 KB
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4. Business Case Preprocessing the Data.mp4
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88.4 MB
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4. Business Case Preprocessing the Data.srt
|
12.6 KB
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4.1 Business Case Preprocessing the Data.html
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0.1 KB
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5. Business Case Preprocessing the Data - Exercise.html
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0.4 KB
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5.1 Business Case Preprocessing the Data.html
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0.1 KB
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6. Business Case Load the Preprocessed Data.mp4
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18.4 MB
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6. Business Case Load the Preprocessed Data.srt
|
4.8 KB
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7. Business Case Load the Preprocessed Data - Exercise.html
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0.1 KB
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7.1 Business Case Load the Preprocessed Data.html
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0.1 KB
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8. Business Case Learning and Interpreting the Result.mp4
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32.7 MB
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8. Business Case Learning and Interpreting the Result.srt
|
6.4 KB
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8.1 Business Case Learning and Interpreting.html
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0.1 KB
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9. Business Case Setting an Early Stopping Mechanism.mp4
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52.2 MB
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9. Business Case Setting an Early Stopping Mechanism.srt
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8.0 KB
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9.1 Business Case Setting an Early Stopping Mechanism.html
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0.1 KB
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/52. Deep Learning - Conclusion/
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1. Summary on What You've Learned.mp4
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41.7 MB
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1. Summary on What You've Learned.srt
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5.3 KB
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2. What's Further out there in terms of Machine Learning.mp4
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21.1 MB
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2. What's Further out there in terms of Machine Learning.srt
|
2.6 KB
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3. DeepMind and Deep Learning.html
|
1.1 KB
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4. An overview of CNNs.mp4
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61.6 MB
|
4. An overview of CNNs.srt
|
6.6 KB
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5. An Overview of RNNs.mp4
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26.5 MB
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5. An Overview of RNNs.srt
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3.8 KB
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6. An Overview of non-NN Approaches.mp4
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47.0 MB
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6. An Overview of non-NN Approaches.srt
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5.2 KB
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/53. Appendix Deep Learning - TensorFlow 1 Introduction/
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1. READ ME!!!!.html
|
0.6 KB
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10. Basic NN Example with TF Exercises.html
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1.6 KB
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10.1 Basic NN Example with TensorFlow Exercise 2.3 Solution.html
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0.2 KB
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10.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html
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0.2 KB
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10.3 Basic NN Example with TensorFlow Exercise 3 Solution.html
|
0.2 KB
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10.4 Basic NN Example with TensorFlow Exercise 1 Solution.html
|
0.2 KB
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10.5 Basic NN Example with TensorFlow (All Exercises).html
|
0.2 KB
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10.6 Basic NN Example with TensorFlow Exercise 4 Solution.html
|
0.2 KB
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10.7 Basic NN Example with TensorFlow Exercise 2.2 Solution.html
|
0.2 KB
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10.8 Basic NN Example with TensorFlow Exercise 2.4 Solution.html
|
0.2 KB
|
2. How to Install TensorFlow 1.mp4
|
11.9 MB
|
2. How to Install TensorFlow 1.srt
|
3.5 KB
|
3. A Note on Installing Packages in Anaconda.html
|
2.4 KB
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4. TensorFlow Intro.mp4
|
50.0 MB
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4. TensorFlow Intro.srt
|
5.3 KB
|
5. Actual Introduction to TensorFlow.mp4
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18.3 MB
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5. Actual Introduction to TensorFlow.srt
|
2.2 KB
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5.1 Actual Introduction to TensorFlow.html
|
0.1 KB
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5.2 Shortcuts-for-Jupyter.pdf.pdf
|
634.0 KB
|
6. Types of File Formats, supporting Tensors.mp4
|
21.3 MB
|
6. Types of File Formats, supporting Tensors.srt
|
3.5 KB
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6.1 Basic NN Example with TensorFlow (Part 1).html
|
0.2 KB
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7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4
|
40.4 MB
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7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt
|
7.5 KB
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7.1 Basic NN Example with TensorFlow (Part 2).html
|
0.2 KB
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8. Basic NN Example with TF Loss Function and Gradient Descent.mp4
|
34.1 MB
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8. Basic NN Example with TF Loss Function and Gradient Descent.srt
|
4.9 KB
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8.1 Basic NN Example with TensorFlow (Part 3).html
|
0.2 KB
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9. Basic NN Example with TF Model Output.mp4
|
39.2 MB
|
9. Basic NN Example with TF Model Output.srt
|
8.1 KB
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9.1 Basic NN Example with TensorFlow (Complete).html
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0.2 KB
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/54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/
|
1. MNIST What is the MNIST Dataset.mp4
|
18.7 MB
|
1. MNIST What is the MNIST Dataset.srt
|
3.6 KB
|
10. MNIST Solutions.html
|
2.2 KB
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10.1 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html
|
0.2 KB
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10.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html
|
0.2 KB
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10.11 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html
|
0.2 KB
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10.2 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html
|
0.2 KB
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10.3 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html
|
0.2 KB
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10.4 TensorFlow MNIST '2. Depth' Solution.html
|
0.2 KB
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10.5 TensorFlow MNIST 'Time' Solution.html
|
0.2 KB
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10.6 TensorFlow MNIST '1. Width' Solution.html
|
0.2 KB
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10.7 TensorFlow MNIST 'Around 98% Accuracy' Solution.html
|
0.2 KB
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10.8 TensorFlow MNIST '3. Width and Depth' Solution.html
|
0.2 KB
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10.9 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html
|
0.2 KB
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11. MNIST Exercises.html
|
2.2 KB
|
11.1 TensorFlow MNIST All Exercises.html
|
0.1 KB
|
2. MNIST How to Tackle the MNIST.mp4
|
23.7 MB
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2. MNIST How to Tackle the MNIST.srt
|
3.7 KB
|
3. MNIST Relevant Packages.mp4
|
19.8 MB
|
3. MNIST Relevant Packages.srt
|
2.2 KB
|
3.1 TensorFlow MNIST Part 1 with Comments.html
|
0.2 KB
|
4. MNIST Model Outline.mp4
|
59.1 MB
|
4. MNIST Model Outline.srt
|
9.3 KB
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4.1 TensorFlow MNIST Part 2 with Comments.html
|
0.2 KB
|
5. MNIST Loss and Optimization Algorithm.mp4
|
27.1 MB
|
5. MNIST Loss and Optimization Algorithm.srt
|
3.6 KB
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5.1 TensorFlow MNIST Part 3 with Comments.html
|
0.2 KB
|
6. Calculating the Accuracy of the Model.mp4
|
46.0 MB
|
6. Calculating the Accuracy of the Model.srt
|
5.3 KB
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6.1 TensorFlow MNIST Part 4 with Comments.html
|
0.2 KB
|
7. MNIST Batching and Early Stopping.mp4
|
13.5 MB
|
7. MNIST Batching and Early Stopping.srt
|
3.0 KB
|
7.1 TensorFlow MNIST Part 5 with Comments.html
|
0.2 KB
|
8. MNIST Learning.mp4
|
49.0 MB
|
8. MNIST Learning.srt
|
10.4 KB
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8.1 TensorFlow MNIST Part 6 with Comments.html
|
0.2 KB
|
9. MNIST Results and Testing.mp4
|
65.8 MB
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9. MNIST Results and Testing.srt
|
8.4 KB
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9.1 TensorFlow MNIST Complete Code with Comments.html
|
0.2 KB
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/55. Appendix Deep Learning - TensorFlow 1 Business Case/
|
1. Business Case Getting acquainted with the dataset.mp4
|
91.9 MB
|
1. Business Case Getting acquainted with the dataset.srt
|
11.0 KB
|
1.1 Audiobooks_data.csv.csv
|
727.8 KB
|
10. Business Case Testing the Model.mp4
|
11.7 MB
|
10. Business Case Testing the Model.srt
|
2.8 KB
|
11. Business Case A Comment on the Homework.mp4
|
38.2 MB
|
11. Business Case A Comment on the Homework.srt
|
5.4 KB
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11.1 TensorFlow Business Case Homework.html
|
0.1 KB
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12. Business Case Final Exercise.html
|
0.4 KB
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12.1 TensorFlow Business Case Homework.html
|
0.1 KB
|
2. Business Case Outlining the Solution.mp4
|
12.8 MB
|
2. Business Case Outlining the Solution.srt
|
2.6 KB
|
3. The Importance of Working with a Balanced Dataset.mp4
|
41.3 MB
|
3. The Importance of Working with a Balanced Dataset.srt
|
4.6 KB
|
4. Business Case Preprocessing.mp4
|
108.4 MB
|
4. Business Case Preprocessing.srt
|
13.8 KB
|
4.1 Audiobooks Preprocessing.html
|
0.1 KB
|
5. Business Case Preprocessing Exercise.html
|
0.4 KB
|
5.1 Preprocessing Exercise.html
|
0.1 KB
|
6. Creating a Data Provider.mp4
|
80.1 MB
|
6. Creating a Data Provider.srt
|
7.9 KB
|
6.1 Creating a Data Provider (Class).html
|
0.1 KB
|
7. Business Case Model Outline.mp4
|
55.7 MB
|
7. Business Case Model Outline.srt
|
7.1 KB
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7.1 TensorFlow Business Case Model Outline.html
|
0.1 KB
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8. Business Case Optimization.mp4
|
43.5 MB
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8. Business Case Optimization.srt
|
6.8 KB
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8.1 TensorFlow Business Case Optimization.html
|
0.1 KB
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9. Business Case Interpretation.mp4
|
27.0 MB
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9. Business Case Interpretation.srt
|
3.0 KB
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9.1 TensorFlow Business Case Interpretation.html
|
0.1 KB
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/56. Software Integration/
|
1. What are Data, Servers, Clients, Requests, and Responses.mp4
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72.4 MB
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1. What are Data, Servers, Clients, Requests, and Responses.srt
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6.1 KB
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10. Software Integration - Explained.html
|
0.2 KB
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2. What are Data, Servers, Clients, Requests, and Responses.html
|
0.2 KB
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3. What are Data Connectivity, APIs, and Endpoints.mp4
|
109.1 MB
|
3. What are Data Connectivity, APIs, and Endpoints.srt
|
8.7 KB
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4. What are Data Connectivity, APIs, and Endpoints.html
|
0.2 KB
|
5. Taking a Closer Look at APIs.mp4
|
121.2 MB
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5. Taking a Closer Look at APIs.srt
|
10.6 KB
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6. Taking a Closer Look at APIs.html
|
0.2 KB
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7. Communication between Software Products through Text Files.mp4
|
63.3 MB
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7. Communication between Software Products through Text Files.srt
|
5.6 KB
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8. Communication between Software Products through Text Files.html
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0.2 KB
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9. Software Integration - Explained.mp4
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66.8 MB
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9. Software Integration - Explained.srt
|
6.9 KB
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/57. Case Study - What's Next in the Course/
|
1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4
|
54.8 MB
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1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt
|
5.6 KB
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2. The Business Task.mp4
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41.1 MB
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2. The Business Task.srt
|
3.8 KB
|
3. Introducing the Data Set.mp4
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42.9 MB
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3. Introducing the Data Set.srt
|
4.2 KB
|
4. Introducing the Data Set.html
|
0.2 KB
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/58. Case Study - Preprocessing the 'Absenteeism_data'/
|
1. What to Expect from the Following Sections.html
|
2.5 KB
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1.1 Absenteeism_data.csv.csv
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32.8 KB
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1.2 data_preprocessing_homework.pdf.pdf
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137.7 KB
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1.3 df_preprocessed.csv.csv
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29.8 KB
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10. Analyzing the Reasons for Absence.mp4
|
42.5 MB
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10. Analyzing the Reasons for Absence.srt
|
6.0 KB
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11. Obtaining Dummies from a Single Feature.mp4
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85.0 MB
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11. Obtaining Dummies from a Single Feature.srt
|
10.4 KB
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12. EXERCISE - Obtaining Dummies from a Single Feature.html
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0.1 KB
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13. SOLUTION - Obtaining Dummies from a Single Feature.html
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0.1 KB
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14. Dropping a Dummy Variable from the Data Set.html
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2.4 KB
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15. More on Dummy Variables A Statistical Perspective.mp4
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14.4 MB
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15. More on Dummy Variables A Statistical Perspective.srt
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1.7 KB
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16. Classifying the Various Reasons for Absence.mp4
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78.2 MB
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16. Classifying the Various Reasons for Absence.srt
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10.3 KB
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17. Using .concat() in Python.mp4
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40.6 MB
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17. Using .concat() in Python.srt
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5.2 KB
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18. EXERCISE - Using .concat() in Python.html
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0.2 KB
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19. SOLUTION - Using .concat() in Python.html
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0.1 KB
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2. Importing the Absenteeism Data in Python.mp4
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24.3 MB
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2. Importing the Absenteeism Data in Python.srt
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4.1 KB
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20. Reordering Columns in a Pandas DataFrame in Python.mp4
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14.7 MB
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20. Reordering Columns in a Pandas DataFrame in Python.srt
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1.9 KB
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21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html
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0.2 KB
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22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html
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0.5 KB
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23. Creating Checkpoints while Coding in Jupyter.mp4
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26.9 MB
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23. Creating Checkpoints while Coding in Jupyter.srt
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3.7 KB
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23.1 Creating Checkpoints.html
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0.2 KB
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24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html
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0.1 KB
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25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html
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0.1 KB
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26. Analyzing the Dates from the Initial Data Set.mp4
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60.1 MB
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26. Analyzing the Dates from the Initial Data Set.srt
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8.6 KB
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27. Extracting the Month Value from the Date Column.mp4
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50.1 MB
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27. Extracting the Month Value from the Date Column.srt
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8.2 KB
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28. Extracting the Day of the Week from the Date Column.mp4
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29.3 MB
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28. Extracting the Day of the Week from the Date Column.srt
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4.6 KB
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29. EXERCISE - Removing the Date Column.html
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1.2 KB
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29.1 Removing the “Date” Column.html
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0.2 KB
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29.2 Preprocessing.html
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0.2 KB
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3. Checking the Content of the Data Set.mp4
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64.9 MB
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3. Checking the Content of the Data Set.srt
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7.2 KB
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30. Analyzing Several Straightforward Columns for this Exercise.mp4
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31.0 MB
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30. Analyzing Several Straightforward Columns for this Exercise.srt
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4.4 KB
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31. Working on Education, Children, and Pets.mp4
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41.5 MB
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31. Working on Education, Children, and Pets.srt
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5.8 KB
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32. Final Remarks of this Section.mp4
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22.7 MB
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32. Final Remarks of this Section.srt
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2.5 KB
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32.1 Exercises and solutions.html
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0.2 KB
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32.2 Preprocessing.html
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0.2 KB
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33. A Note on Exporting Your Data as a .csv File.html
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0.9 KB
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4. Introduction to Terms with Multiple Meanings.mp4
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29.2 MB
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4. Introduction to Terms with Multiple Meanings.srt
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4.2 KB
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5. What's Regression Analysis - a Quick Refresher.html
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2.9 KB
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6. Using a Statistical Approach towards the Solution to the Exercise.mp4
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21.2 MB
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6. Using a Statistical Approach towards the Solution to the Exercise.srt
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2.9 KB
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7. Dropping a Column from a DataFrame in Python.mp4
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64.8 MB
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7. Dropping a Column from a DataFrame in Python.srt
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8.0 KB
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8. EXERCISE - Dropping a Column from a DataFrame in Python.html
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0.9 KB
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9. SOLUTION - Dropping a Column from a DataFrame in Python.html
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0.1 KB
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/59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/
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1. Exploring the Problem with a Machine Learning Mindset.mp4
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28.9 MB
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1. Exploring the Problem with a Machine Learning Mindset.srt
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4.7 KB
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1.1 Absenteeism_preprocessed.csv.csv
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29.8 KB
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10. Interpreting the Coefficients of the Logistic Regression.mp4
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42.4 MB
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10. Interpreting the Coefficients of the Logistic Regression.srt
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7.4 KB
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11. Backward Elimination or How to Simplify Your Model.mp4
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41.5 MB
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11. Backward Elimination or How to Simplify Your Model.srt
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5.4 KB
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11.1 Logistic Regression prior to Backward Elimination.html
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0.2 KB
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12. Testing the Model We Created.mp4
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51.4 MB
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12. Testing the Model We Created.srt
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6.7 KB
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13. Saving the Model and Preparing it for Deployment.mp4
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39.3 MB
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13. Saving the Model and Preparing it for Deployment.srt
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5.7 KB
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14. ARTICLE - A Note on 'pickling'.html
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2.2 KB
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15. EXERCISE - Saving the Model (and Scaler).html
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0.3 KB
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15.1 Logistic Regression with Comments.html
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0.2 KB
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15.2 Logistic Regression.html
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0.2 KB
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16. Preparing the Deployment of the Model through a Module.mp4
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46.6 MB
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16. Preparing the Deployment of the Model through a Module.srt
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5.8 KB
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2. Creating the Targets for the Logistic Regression.mp4
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48.0 MB
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2. Creating the Targets for the Logistic Regression.srt
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8.6 KB
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3. Selecting the Inputs for the Logistic Regression.mp4
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17.6 MB
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3. Selecting the Inputs for the Logistic Regression.srt
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3.7 KB
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4. Standardizing the Data.mp4
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21.6 MB
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4. Standardizing the Data.srt
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4.3 KB
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5. Splitting the Data for Training and Testing.mp4
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55.3 MB
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5. Splitting the Data for Training and Testing.srt
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8.3 KB
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6. Fitting the Model and Assessing its Accuracy.mp4
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43.6 MB
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6. Fitting the Model and Assessing its Accuracy.srt
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7.6 KB
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7. Creating a Summary Table with the Coefficients and Intercept.mp4
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40.8 MB
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7. Creating a Summary Table with the Coefficients and Intercept.srt
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6.8 KB
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8. Interpreting the Coefficients for Our Problem.mp4
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54.9 MB
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8. Interpreting the Coefficients for Our Problem.srt
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8.1 KB
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9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4
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43.2 MB
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9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt
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5.1 KB
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9.1 Logistic Regression prior to Custom Scaler.html
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0.2 KB
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/6. The Field of Data Science - Popular Data Science Tools/
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1. Necessary Programming Languages and Software Used in Data Science.mp4
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108.5 MB
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1. Necessary Programming Languages and Software Used in Data Science.srt
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7.5 KB
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2. Necessary Programming Languages and Software Used in Data Science.html
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0.2 KB
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/60. Case Study - Loading the 'absenteeism_module'/
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1. Are You Sure You're All Set.html
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0.5 KB
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1.1 5 Files Needed to Deploy the Model.html
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0.1 KB
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2. Deploying the 'absenteeism_module' - Part I.mp4
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26.7 MB
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2. Deploying the 'absenteeism_module' - Part I.srt
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4.9 KB
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3. Deploying the 'absenteeism_module' - Part II.mp4
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56.9 MB
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3. Deploying the 'absenteeism_module' - Part II.srt
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7.7 KB
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4. Exporting the Obtained Data Set as a .csv.html
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1.0 KB
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4.1 Deploying the ‘absenteeism_module.html
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0.2 KB
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/61. Case Study - Analyzing the Predicted Outputs in Tableau/
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1. EXERCISE - Age vs Probability.html
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0.4 KB
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2. Analyzing Age vs Probability in Tableau.mp4
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59.3 MB
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2. Analyzing Age vs Probability in Tableau.srt
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10.3 KB
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3. EXERCISE - Reasons vs Probability.html
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0.4 KB
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4. Analyzing Reasons vs Probability in Tableau.mp4
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62.2 MB
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4. Analyzing Reasons vs Probability in Tableau.srt
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9.8 KB
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5. EXERCISE - Transportation Expense vs Probability.html
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0.6 KB
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6. Analyzing Transportation Expense vs Probability in Tableau.mp4
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42.6 MB
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6. Analyzing Transportation Expense vs Probability in Tableau.srt
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7.4 KB
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/62. Bonus lecture/
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1. Bonus Lecture Next Steps.html
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2.6 KB
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/7. The Field of Data Science - Careers in Data Science/
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1. Finding the Job - What to Expect and What to Look for.mp4
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57.0 MB
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1. Finding the Job - What to Expect and What to Look for.srt
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4.6 KB
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2. Finding the Job - What to Expect and What to Look for.html
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0.2 KB
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/8. The Field of Data Science - Debunking Common Misconceptions/
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1. Debunking Common Misconceptions.mp4
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76.4 MB
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1. Debunking Common Misconceptions.srt
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5.4 KB
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2. Debunking Common Misconceptions.html
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0.2 KB
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/9. Part 2 Probability/
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1. The Basic Probability Formula.mp4
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90.1 MB
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1. The Basic Probability Formula.srt
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9.1 KB
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1.1 Course Notes - Basic Probability.pdf.pdf
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380.0 KB
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2. The Basic Probability Formula.html
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0.2 KB
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3. Computing Expected Values.mp4
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79.4 MB
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3. Computing Expected Values.srt
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6.8 KB
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4. Computing Expected Values.html
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0.2 KB
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5. Frequency.mp4
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64.7 MB
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5. Frequency.srt
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6.6 KB
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6. Frequency.html
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0.2 KB
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7. Events and Their Complements.mp4
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62.0 MB
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7. Events and Their Complements.srt
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6.9 KB
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8. Events and Their Complements.html
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0.2 KB
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/
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[FreeAllCourse.Com].URL
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0.2 KB
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Total files 1327
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