FreeCourseLab com Udemy The Data Science Course 2019 Complete Data Science Bootcamp

FreeCourseLab com Udemy The Data Science Course 2019 Complete Data Science Bootcamp

Name	[FreeCourseLab.com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	15.5 GB
Total Files	1226
Hash	2C9E646309E1944DC4C43B1CA353CAECBBABD04E

/1. Part 1 Introduction/
1. A Practical Example What You Will Learn in This Course.mp4	51.4 MB
1. A Practical Example What You Will Learn in This Course.vtt	5.8 KB
2. What Does the Course Cover.mp4	65.3 MB
2. What Does the Course Cover.vtt	4.6 KB
3. Download All Resources and Important FAQ.html	21.3 KB
3.1 Download All Resources.html	0.1 KB
3.2 FAQ_The_Data_Science_Course.pdf.pdf	313.4 KB
/10. Combinatorics/
1. Fundamentals of Combinatorics.mp4	17.0 MB
1. Fundamentals of Combinatorics.vtt	1.2 KB
1.1 Course Notes - Combinatorics.pdf.pdf	231.5 KB
10. Solving Variations without Repetition.html	0.2 KB
11. Solving Combinations.mp4	60.1 MB
11. Solving Combinations.vtt	5.1 KB
11.1 Combinations With Repetition.pdf.pdf	212.4 KB
12. Solving Combinations.html	0.2 KB
13. Symmetry of Combinations.mp4	40.6 MB
13. Symmetry of Combinations.vtt	3.9 KB
13.1 Symmetry Explained.pdf.pdf	87.1 KB
14. Symmetry of Combinations.html	0.2 KB
15. Solving Combinations with Separate Sample Spaces.mp4	34.8 MB
15. Solving Combinations with Separate Sample Spaces.vtt	3.4 KB
16. Solving Combinations with Separate Sample Spaces.html	0.2 KB
17. Combinatorics in Real-Life The Lottery.mp4	41.3 MB
17. Combinatorics in Real-Life The Lottery.vtt	3.7 KB
18. Combinatorics in Real-Life The Lottery.html	0.2 KB
19. A Recap of Combinatorics.mp4	40.4 MB
19. A Recap of Combinatorics.vtt	3.3 KB
2. Fundamentals of Combinatorics.html	0.2 KB
20. A Practical Example of Combinatorics.mp4	140.7 MB
20. A Practical Example of Combinatorics.vtt	12.7 KB
20.1 Additional Exercises Combinatorics Solutions.pdf.pdf	251.7 KB
20.2 Additional Exercises Combinatorics.pdf.pdf	109.1 KB
3. Permutations and How to Use Them.mp4	44.8 MB
3. Permutations and How to Use Them.vtt	3.7 KB
4. Permutations and How to Use Them.html	0.2 KB
5. Simple Operations with Factorials.mp4	37.9 MB
5. Simple Operations with Factorials.vtt	3.0 KB
6. Simple Operations with Factorials.html	0.2 KB
7. Solving Variations with Repetition.mp4	35.7 MB
7. Solving Variations with Repetition.vtt	3.2 KB
8. Solving Variations with Repetition.html	0.2 KB
9. Solving Variations without Repetition.mp4	45.2 MB
9. Solving Variations without Repetition.vtt	4.1 KB
/11. Bayesian Inference/
1. Sets and Events.mp4	56.1 MB
1. Sets and Events.vtt	4.6 KB
1.1 Course Notes - Bayesian Inference.pdf.pdf	395.3 KB
10. Mutually Exclusive Sets.html	0.2 KB
11. Dependence and Independence of Sets.mp4	36.5 MB
11. Dependence and Independence of Sets.vtt	3.1 KB
12. Dependence and Independence of Sets.html	0.2 KB
13. The Conditional Probability Formula.mp4	48.1 MB
13. The Conditional Probability Formula.vtt	4.5 KB
14. The Conditional Probability Formula.html	0.2 KB
15. The Law of Total Probability.mp4	36.6 MB
15. The Law of Total Probability.vtt	3.2 KB
16. The Additive Rule.mp4	27.0 MB
16. The Additive Rule.vtt	2.4 KB
17. The Additive Rule.html	0.2 KB
18. The Multiplication Law.mp4	45.0 MB
18. The Multiplication Law.vtt	4.2 KB
19. The Multiplication Law.html	0.2 KB
2. Sets and Events.html	0.2 KB
20. Bayes' Law.mp4	62.5 MB
20. Bayes' Law.vtt	6.5 KB
21. Bayes' Law.html	0.2 KB
22. A Practical Example of Bayesian Inference.mp4	163.0 MB
22. A Practical Example of Bayesian Inference.vtt	17.5 KB
22.1 CDS_2017-2018 Hamilton.pdf.pdf	865.6 KB
3. Ways Sets Can Interact.mp4	47.6 MB
3. Ways Sets Can Interact.vtt	4.0 KB
4. Ways Sets Can Interact.html	0.2 KB
5. Intersection of Sets.mp4	28.3 MB
5. Intersection of Sets.vtt	2.2 KB
6. Intersection of Sets.html	0.2 KB
7. Union of Sets.mp4	60.0 MB
7. Union of Sets.vtt	5.1 KB
8. Union of Sets.html	0.2 KB
9. Mutually Exclusive Sets.mp4	26.6 MB
9. Mutually Exclusive Sets.vtt	2.3 KB
/12. Probability Distributions/
1. Fundamentals of Probability Distributions.mp4	77.0 MB
1. Fundamentals of Probability Distributions.vtt	6.9 KB
1.1 Course Notes - Probability Distributions.pdf.pdf	458.8 KB
10. Discrete Distributions The Bernoulli Distribution.html	0.2 KB
11. Discrete Distributions The Binomial Distribution.mp4	68.7 MB
11. Discrete Distributions The Binomial Distribution.vtt	7.6 KB
12. Discrete Distributions The Binomial Distribution.html	0.2 KB
13. Discrete Distributions The Poisson Distribution.mp4	61.3 MB
13. Discrete Distributions The Poisson Distribution.vtt	5.9 KB
13.1 Poisson - Expected Value and Variance.pdf.pdf	149.5 KB
14. Discrete Distributions The Poisson Distribution.html	0.2 KB
15. Characteristics of Continuous Distributions.mp4	83.6 MB
15. Characteristics of Continuous Distributions.vtt	7.8 KB
15.1 Solving Integrals.pdf.pdf	352.1 KB
16. Characteristics of Continuous Distributions.html	0.2 KB
17. Continuous Distributions The Normal Distribution.mp4	50.6 MB
17. Continuous Distributions The Normal Distribution.vtt	4.3 KB
17.1 Normal Distribution - Exp and Var.pdf.pdf	147.5 KB
18. Continuous Distributions The Normal Distribution.html	0.2 KB
19. Continuous Distributions The Standard Normal Distribution.mp4	50.2 MB
19. Continuous Distributions The Standard Normal Distribution.vtt	4.8 KB
2. Fundamentals of Probability Distributions.html	0.2 KB
20. Continuous Distributions The Standard Normal Distribution.html	0.2 KB
21. Continuous Distributions The Students' T Distribution.mp4	28.5 MB
21. Continuous Distributions The Students' T Distribution.vtt	2.6 KB
22. Continuous Distributions The Students' T Distribution.html	0.2 KB
23. Continuous Distributions The Chi-Squared Distribution.mp4	27.6 MB
23. Continuous Distributions The Chi-Squared Distribution.vtt	2.5 KB
24. Continuous Distributions The Chi-Squared Distribution.html	0.2 KB
25. Continuous Distributions The Exponential Distribution.mp4	42.2 MB
25. Continuous Distributions The Exponential Distribution.vtt	3.7 KB
26. Continuous Distributions The Exponential Distribution.html	0.2 KB
27. Continuous Distributions The Logistic Distribution.mp4	49.3 MB
27. Continuous Distributions The Logistic Distribution.vtt	4.6 KB
28. Continuous Distributions The Logistic Distribution.html	0.2 KB
29. A Practical Example of Probability Distributions.mp4	165.7 MB
29. A Practical Example of Probability Distributions.vtt	17.9 KB
29.1 FIFA19 (post).csv.csv	9.1 MB
29.2 Daily Views.xlsx.xlsx	9.8 KB
29.3 FIFA19.csv.csv	9.1 MB
29.4 Customers_Membership (post).xlsx.xlsx	16.0 KB
29.5 Customers_Membership.xlsx.xlsx	9.9 KB
29.6 Daily Views (post).xlsx.xlsx	20.7 KB
3. Types of Probability Distributions.mp4	96.8 MB
3. Types of Probability Distributions.vtt	8.5 KB
4. Types of Probability Distributions.html	0.2 KB
5. Characteristics of Discrete Distributions.mp4	23.8 MB
5. Characteristics of Discrete Distributions.vtt	2.2 KB
6. Characteristics of Discrete Distributions.html	0.2 KB
7. Discrete Distributions The Uniform Distribution.mp4	25.6 MB
7. Discrete Distributions The Uniform Distribution.vtt	2.5 KB
8. Discrete Distributions The Uniform Distribution.html	0.2 KB
9. Discrete Distributions The Bernoulli Distribution.mp4	35.8 MB
9. Discrete Distributions The Bernoulli Distribution.vtt	3.6 KB
/13. Probability in Other Fields/
1. Probability in Finance.mp4	103.9 MB
1. Probability in Finance.vtt	8.9 KB
2. Probability in Statistics.mp4	81.0 MB
2. Probability in Statistics.vtt	7.7 KB
3. Probability in Data Science.mp4	66.6 MB
3. Probability in Data Science.vtt	6.0 KB
/14. Part 3 Statistics/
1. Population and Sample.mp4	60.9 MB
1. Population and Sample.vtt	4.9 KB
1.1 Statistics Glossary.xlsx.xlsx	31.0 KB
1.2 Course notes_descriptive_statistics.pdf.pdf	493.8 KB
2. Population and Sample.html	0.2 KB
/15. Statistics - Descriptive Statistics/
1. Types of Data.mp4	76.0 MB
1. Types of Data.vtt	5.4 KB
1.1 Glossary.xlsx.xlsx	20.4 KB
1.2 Course notes_descriptive_statistics.pdf.pdf	493.8 KB
10. Numerical Variables Exercise.html	0.1 KB
10.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx	13.5 KB
10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx	12.0 KB
11. The Histogram.mp4	14.5 MB
11. The Histogram.vtt	2.7 KB
11.1 2.5. The Histogram_lesson.xlsx.xlsx	19.1 KB
12. The Histogram.html	0.2 KB
13. Histogram Exercise.html	0.1 KB
13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx	17.5 KB
13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf	296.1 KB
13.3 2.5.The-Histogram-exercise.xlsx.xlsx	15.9 KB
14. Cross Tables and Scatter Plots.mp4	41.7 MB
14. Cross Tables and Scatter Plots.vtt	6.0 KB
14.1 2.6. Cross table and scatter plot.xlsx.xlsx	26.7 KB
15. Cross Tables and Scatter Plots.html	0.2 KB
16. Cross Tables and Scatter Plots Exercise.html	0.1 KB
16.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx	41.4 KB
16.2 2.6. Cross table and scatter plot_exercise.xlsx.xlsx	16.7 KB
17. Mean, median and mode.mp4	38.9 MB
17. Mean, median and mode.vtt	5.1 KB
17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx	10.7 KB
18. Mean, Median and Mode Exercise.html	0.1 KB
18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx	11.1 KB
18.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx	11.6 KB
19. Skewness.mp4	20.3 MB
19. Skewness.vtt	3.3 KB
19.1 2.8. Skewness_lesson.xlsx.xlsx	35.5 KB
2. Types of Data.html	0.2 KB
20. Skewness.html	0.2 KB
21. Skewness Exercise.html	0.1 KB
21.1 2.8. Skewness_exercise_solution.xlsx.xlsx	20.3 KB
21.2 2.8. Skewness_exercise.xlsx.xlsx	9.7 KB
22. Variance.mp4	53.4 MB
22. Variance.vtt	6.8 KB
22.1 2.9. Variance_lesson.xlsx.xlsx	10.3 KB
23. Variance Exercise.html	0.5 KB
23.1 2.9. Variance_exercise.xlsx.xlsx	11.1 KB
23.2 2.9. Variance_exercise_solution.xlsx.xlsx	11.3 KB
24. Standard Deviation and Coefficient of Variation.mp4	47.3 MB
24. Standard Deviation and Coefficient of Variation.vtt	5.9 KB
24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx	11.2 KB
25. Standard Deviation.html	0.2 KB
26. Standard Deviation and Coefficient of Variation Exercise.html	0.1 KB
26.1 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx.xlsx	12.7 KB
26.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx	11.6 KB
27. Covariance.mp4	28.8 MB
27. Covariance.vtt	4.4 KB
27.1 2.11. Covariance_lesson.xlsx.xlsx	25.5 KB
28. Covariance.html	0.2 KB
29. Covariance Exercise.html	0.1 KB
29.1 2.11. Covariance_exercise.xlsx.xlsx	20.7 KB
29.2 2.11. Covariance_exercise_solution.xlsx.xlsx	30.2 KB
3. Levels of Measurement.mp4	57.0 MB
3. Levels of Measurement.vtt	4.1 KB
30. Correlation Coefficient.mp4	30.8 MB
30. Correlation Coefficient.vtt	4.2 KB
31. Correlation.html	0.2 KB
32. Correlation Coefficient Exercise.html	0.1 KB
32.1 2.12. Correlation_exercise_solution.xlsx.xlsx	30.2 KB
32.2 2.12. Correlation_exercise.xlsx.xlsx	30.0 KB
4. Levels of Measurement.html	0.2 KB
5. Categorical Variables - Visualization Techniques.mp4	40.3 MB
5. Categorical Variables - Visualization Techniques.vtt	5.8 KB
5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx	31.5 KB
6. Categorical Variables - Visualization Techniques.html	0.2 KB
7. Categorical Variables Exercise.html	0.1 KB
7.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx	42.1 KB
7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf	296.1 KB
7.3 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx	15.6 KB
8. Numerical Variables - Frequency Distribution Table.mp4	27.1 MB
8. Numerical Variables - Frequency Distribution Table.vtt	3.9 KB
8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx	11.7 KB
9. Numerical Variables - Frequency Distribution Table.html	0.2 KB
/16. Statistics - Practical Example Descriptive Statistics/
1. Practical Example Descriptive Statistics.mp4	168.3 MB
1. Practical Example Descriptive Statistics.vtt	18.4 KB
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx	150.0 KB
2. Practical Example Descriptive Statistics Exercise.html	0.1 KB
2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx	149.7 KB
2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx	123.2 KB
/17. Statistics - Inferential Statistics Fundamentals/
1. Introduction.mp4	16.3 MB
1. Introduction.vtt	1.5 KB
1.1 Course notes_inferential statistics.pdf.pdf	391.5 KB
10. Central Limit Theorem.html	0.2 KB
11. Standard error.mp4	23.9 MB
11. Standard error.vtt	1.8 KB
12. Standard Error.html	0.2 KB
13. Estimators and Estimates.mp4	50.2 MB
13. Estimators and Estimates.vtt	3.4 KB
14. Estimators and Estimates.html	0.2 KB
2. What is a Distribution.mp4	64.6 MB
2. What is a Distribution.vtt	5.2 KB
2.1 Course notes_inferential statistics.pdf.pdf	391.5 KB
2.2 3.2. What is a distribution_lesson.xlsx.xlsx	19.9 KB
3. What is a Distribution.html	0.2 KB
4. The Normal Distribution.mp4	52.3 MB
4. The Normal Distribution.vtt	4.4 KB
5. The Normal Distribution.html	0.2 KB
6. The Standard Normal Distribution.mp4	23.6 MB
6. The Standard Normal Distribution.vtt	3.5 KB
6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx	10.6 KB
7. The Standard Normal Distribution.html	0.2 KB
8. The Standard Normal Distribution Exercise.html	0.1 KB
8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx	12.3 KB
8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx	24.6 KB
9. Central Limit Theorem.mp4	65.9 MB
9. Central Limit Theorem.vtt	5.1 KB
/18. Statistics - Inferential Statistics Confidence Intervals/
1. What are Confidence Intervals.mp4	52.4 MB
1. What are Confidence Intervals.vtt	2.9 KB
10. Margin of Error.mp4	62.0 MB
10. Margin of Error.vtt	5.5 KB
11. Margin of Error.html	0.2 KB
12. Confidence intervals. Two means. Dependent samples.mp4	73.9 MB
12. Confidence intervals. Two means. Dependent samples.vtt	7.3 KB
12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx	10.7 KB
13. Confidence intervals. Two means. Dependent samples Exercise.html	0.1 KB
13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx	14.6 KB
13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx	14.1 KB
14. Confidence intervals. Two means. Independent samples (Part 1).mp4	30.2 MB
14. Confidence intervals. Two means. Independent samples (Part 1).vtt	5.5 KB
14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx	10.1 KB
15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html	0.1 KB
15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx	10.4 KB
15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx	10.1 KB
16. Confidence intervals. Two means. Independent samples (Part 2).mp4	28.1 MB
16. Confidence intervals. Two means. Independent samples (Part 2).vtt	4.1 KB
16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx	9.7 KB
17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html	0.1 KB
17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx	9.4 KB
17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx	10.0 KB
18. Confidence intervals. Two means. Independent samples (Part 3).mp4	20.9 MB
18. Confidence intervals. Two means. Independent samples (Part 3).vtt	1.8 KB
2. What are Confidence Intervals.html	0.2 KB
3. Confidence Intervals; Population Variance Known; z-score.mp4	82.0 MB
3. Confidence Intervals; Population Variance Known; z-score.vtt	8.9 KB
3.1 3.9.The-z-table.xlsx.xlsx	26.2 KB
3.2 3.9. Population variance known, z-score_lesson.xlsx.xlsx	11.5 KB
4. Confidence Intervals; Population Variance Known; z-score; Exercise.html	0.1 KB
4.1 3.9.The-z-table.xlsx.xlsx	26.2 KB
4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx	11.1 KB
4.3 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx	11.4 KB
5. Confidence Interval Clarifications.mp4	59.8 MB
5. Confidence Interval Clarifications.vtt	4.9 KB
6. Student's T Distribution.mp4	37.2 MB
6. Student's T Distribution.vtt	3.8 KB
7. Student's T Distribution.html	0.2 KB
8. Confidence Intervals; Population Variance Unknown; t-score.mp4	33.8 MB
8. Confidence Intervals; Population Variance Unknown; t-score.vtt	5.1 KB
8.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx	11.0 KB
8.2 3.11. The t-table.xlsx.xlsx	16.2 KB
9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html	0.1 KB
9.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx	11.4 KB
9.2 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx	10.9 KB
/19. Statistics - Practical Example Inferential Statistics/
1. Practical Example Inferential Statistics.mp4	107.7 MB
1. Practical Example Inferential Statistics.vtt	12.2 KB
1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx	1.8 MB
2. Practical Example Inferential Statistics Exercise.html	0.1 KB
2.1 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx	1.9 MB
2.2 3.17.Practical-example.Confidence-intervals-exercise.xlsx.xlsx	1.8 MB
/2. The Field of Data Science - The Various Data Science Disciplines/
1. Data Science and Business Buzzwords Why are there so many.mp4	85.4 MB
1. Data Science and Business Buzzwords Why are there so many.vtt	6.0 KB
10. A Breakdown of our Data Science Infographic.html	0.2 KB
2. Data Science and Business Buzzwords Why are there so many.html	0.2 KB
3. What is the difference between Analysis and Analytics.mp4	56.2 MB
3. What is the difference between Analysis and Analytics.vtt	4.5 KB
4. What is the difference between Analysis and Analytics.html	0.2 KB
5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4	67.7 MB
5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt	9.5 KB
5.1 365_DataScience_Diagram.pdf.pdf	330.8 KB
6. Business Analytics, Data Analytics, and Data Science An Introduction.html	0.2 KB
7. Continuing with BI, ML, and AI.mp4	114.3 MB
7. Continuing with BI, ML, and AI.vtt	10.7 KB
7.1 365_DataScience_Diagram.pdf.pdf	330.8 KB
7.2 365_DataScience.png.png	7.3 MB
8. Continuing with BI, ML, and AI.html	0.2 KB
9. A Breakdown of our Data Science Infographic.mp4	71.0 MB
9. A Breakdown of our Data Science Infographic.vtt	4.6 KB
9.1 365_DataScience.png.png	7.3 MB
/20. Statistics - Hypothesis Testing/
1. Null vs Alternative Hypothesis.mp4	96.5 MB
1. Null vs Alternative Hypothesis.vtt	6.3 KB
1.1 Course notes_hypothesis_testing.pdf.pdf	663.8 KB
10. p-value.mp4	58.6 MB
10. p-value.vtt	4.6 KB
10.1 Online p-value calculator.pdf.pdf	1.2 MB
11. p-value.html	0.2 KB
12. Test for the Mean. Population Variance Unknown.mp4	42.2 MB
12. Test for the Mean. Population Variance Unknown.vtt	5.2 KB
12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx	14.9 KB
13. Test for the Mean. Population Variance Unknown Exercise.html	0.1 KB
13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx	11.6 KB
13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx	12.9 KB
14. Test for the Mean. Dependent Samples.mp4	52.8 MB
14. Test for the Mean. Dependent Samples.vtt	5.7 KB
14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx	10.0 KB
15. Test for the Mean. Dependent Samples Exercise.html	0.1 KB
15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx	13.1 KB
15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx	14.7 KB
16. Test for the mean. Independent samples (Part 1).mp4	31.4 MB
16. Test for the mean. Independent samples (Part 1).vtt	4.9 KB
16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx	9.9 KB
17. Test for the mean. Independent samples (Part 1). Exercise.html	0.1 KB
17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx	11.0 KB
17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx	11.5 KB
18. Test for the mean. Independent samples (Part 2).mp4	38.2 MB
18. Test for the mean. Independent samples (Part 2).vtt	4.7 KB
18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx	9.5 KB
19. Test for the mean. Independent samples (Part 2).html	0.2 KB
2. Further Reading on Null and Alternative Hypothesis.html	2.3 KB
20. Test for the mean. Independent samples (Part 2) Exercise.html	0.1 KB
20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx	10.8 KB
20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx	11.7 KB
3. Null vs Alternative Hypothesis.html	0.2 KB
4. Rejection Region and Significance Level.mp4	118.1 MB
4. Rejection Region and Significance Level.vtt	7.9 KB
4.1 Course notes_hypothesis_testing.pdf.pdf	663.8 KB
5. Rejection Region and Significance Level.html	0.2 KB
6. Type I Error and Type II Error.mp4	46.1 MB
6. Type I Error and Type II Error.vtt	5.0 KB
7. Type I Error and Type II Error.html	0.2 KB
8. Test for the Mean. Population Variance Known.mp4	56.9 MB
8. Test for the Mean. Population Variance Known.vtt	7.3 KB
8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx	11.2 KB
9. Test for the Mean. Population Variance Known Exercise.html	0.1 KB
9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx	11.3 KB
9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx	11.5 KB
/21. Statistics - Practical Example Hypothesis Testing/
1. Practical Example Hypothesis Testing.mp4	72.9 MB
1. Practical Example Hypothesis Testing.vtt	7.6 KB
1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx	53.0 KB
2. Practical Example Hypothesis Testing Exercise.html	0.1 KB
2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx	44.4 KB
2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx	45.1 KB
/22. Part 4 Introduction to Python/
1. Introduction to Programming.mp4	61.4 MB
1. Introduction to Programming.vtt	6.2 KB
10. Jupyter's Interface.html	0.2 KB
11. Python 2 vs Python 3.mp4	11.8 MB
11. Python 2 vs Python 3.vtt	3.0 KB
11.1 Python Introduction - Course Notes.pdf.pdf	2.1 MB
2. Introduction to Programming.html	0.2 KB
3. Why Python.mp4	78.7 MB
3. Why Python.vtt	6.3 KB
4. Why Python.html	0.2 KB
5. Why Jupyter.mp4	46.5 MB
5. Why Jupyter.vtt	4.2 KB
6. Why Jupyter.html	0.2 KB
7. Installing Python and Jupyter.mp4	53.5 MB
7. Installing Python and Jupyter.vtt	8.0 KB
8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4	14.5 MB
8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt	3.3 KB
9. Prerequisites for Coding in the Jupyter Notebooks.mp4	32.1 MB
9. Prerequisites for Coding in the Jupyter Notebooks.vtt	7.0 KB
/23. Python - Variables and Data Types/
1. Variables.mp4	26.5 MB
1. Variables.vtt	5.4 KB
1.1 Python Introduction - Course Notes.pdf.pdf	2.1 MB
1.2 Variables - Resources.html	0.1 KB
2. Variables.html	0.2 KB
3. Numbers and Boolean Values in Python.mp4	17.9 MB
3. Numbers and Boolean Values in Python.vtt	3.2 KB
3.1 Numbers and Boolean Values - Resources.html	0.1 KB
4. Numbers and Boolean Values in Python.html	0.2 KB
5. Python Strings.mp4	32.3 MB
5. Python Strings.vtt	6.6 KB
5.1 Strings - Resources.html	0.1 KB
6. Python Strings.html	0.2 KB
/24. Python - Basic Python Syntax/
1. Using Arithmetic Operators in Python.mp4	19.8 MB
1. Using Arithmetic Operators in Python.vtt	3.7 KB
1.1 Arithmetic Operators - Resources.html	0.1 KB
10. Indexing Elements.mp4	6.2 MB
10. Indexing Elements.vtt	1.5 KB
10.1 Indexing Elements - Resources.html	0.1 KB
11. Indexing Elements.html	0.2 KB
12. Structuring with Indentation.mp4	7.2 MB
12. Structuring with Indentation.vtt	2.0 KB
12.1 Structure Your Code with Indentation - Resources.html	0.1 KB
13. Structuring with Indentation.html	0.2 KB
2. Using Arithmetic Operators in Python.html	0.2 KB
3. The Double Equality Sign.mp4	6.3 MB
3. The Double Equality Sign.vtt	1.6 KB
3.1 The Double Equality Sign - Resources.html	0.1 KB
4. The Double Equality Sign.html	0.2 KB
5. How to Reassign Values.mp4	4.2 MB
5. How to Reassign Values.vtt	1.2 KB
5.1 Reassign Values - Resources.html	0.1 KB
6. How to Reassign Values.html	0.2 KB
7. Add Comments.mp4	5.2 MB
7. Add Comments.vtt	1.5 KB
7.1 Add Comments - Resources.html	0.1 KB
8. Add Comments.html	0.2 KB
9. Understanding Line Continuation.mp4	2.5 MB
9. Understanding Line Continuation.vtt	1.0 KB
9.1 Line Continuation - Resources.html	0.1 KB
/25. Python - Other Python Operators/
1. Comparison Operators.mp4	10.7 MB
1. Comparison Operators.vtt	2.2 KB
1.1 Comparison Operators - Resources.html	0.1 KB
2. Comparison Operators.html	0.2 KB
3. Logical and Identity Operators.mp4	31.5 MB
3. Logical and Identity Operators.vtt	5.1 KB
3.1 Logical and Identity Operators - Resources.html	0.1 KB
4. Logical and Identity Operators.html	0.2 KB
/26. Python - Conditional Statements/
1. The IF Statement.mp4	14.3 MB
1. The IF Statement.vtt	3.2 KB
1.1 Introduction to the If Statement - Resources.html	0.1 KB
2. The IF Statement.html	0.2 KB
3. The ELSE Statement.mp4	14.2 MB
3. The ELSE Statement.vtt	2.5 KB
3.1 Add an Else Statement - Resources.html	0.1 KB
4. The ELIF Statement.mp4	34.8 MB
4. The ELIF Statement.vtt	5.9 KB
4.1 Else if, for Brief - Elif - Resources.html	0.1 KB
5. A Note on Boolean Values.mp4	11.8 MB
5. A Note on Boolean Values.vtt	2.6 KB
5.1 A Note on Boolean Values - Resources.html	0.1 KB
6. A Note on Boolean Values.html	0.2 KB
/27. Python - Python Functions/
1. Defining a Function in Python.mp4	8.1 MB
1. Defining a Function in Python.vtt	2.3 KB
1.1 Defining a Function in Python - Resources.html	0.1 KB
2. How to Create a Function with a Parameter.mp4	25.0 MB
2. How to Create a Function with a Parameter.vtt	3.9 KB
2.1 Creating a Function with a Parameter - Resources.html	0.1 KB
3. Defining a Function in Python - Part II.mp4	15.5 MB
3. Defining a Function in Python - Part II.vtt	2.8 KB
3.1 Another Way to Define a Function - Resources.html	0.1 KB
4. How to Use a Function within a Function.mp4	8.5 MB
4. How to Use a Function within a Function.vtt	1.8 KB
4.1 Using a Function in Another Function - Resources.html	0.1 KB
5. Conditional Statements and Functions.mp4	16.4 MB
5. Conditional Statements and Functions.vtt	3.1 KB
5.1 Combining Conditional Statements and Functions - Resources.html	0.1 KB
6. Functions Containing a Few Arguments.mp4	7.9 MB
6. Functions Containing a Few Arguments.vtt	1.2 KB
6.1 Creating Functions Containing a Few Arguments - Resources.html	0.1 KB
7. Built-in Functions in Python.mp4	23.1 MB
7. Built-in Functions in Python.vtt	3.8 KB
7.1 Notable Built-In Functions in Python - Resources.html	0.1 KB
8. Python Functions.html	0.2 KB
/28. Python - Sequences/
1. Lists.mp4	23.1 MB
1. Lists.vtt	4.4 KB
1.1 Lists - Resources.html	0.1 KB
2. Lists.html	0.2 KB
3. Using Methods.mp4	23.0 MB
3. Using Methods.vtt	3.6 KB
3.1 Help Yourself with Methods - Resources.html	0.1 KB
4. Using Methods.html	0.2 KB
5. List Slicing.mp4	32.3 MB
5. List Slicing.vtt	4.9 KB
5.1 List Slicing - Resources.html	0.1 KB
6. Tuples.mp4	17.5 MB
6. Tuples.vtt	3.0 KB
6.1 Tuples - Resources.html	0.1 KB
7. Dictionaries.mp4	26.3 MB
7. Dictionaries.vtt	3.7 KB
7.1 Dictionaries - Resources.html	0.1 KB
8. Dictionaries.html	0.2 KB
/29. Python - Iterations/
1. For Loops.mp4	12.4 MB
1. For Loops.vtt	2.5 KB
1.1 For Loops - Resources.html	0.1 KB
2. For Loops.html	0.2 KB
3. While Loops and Incrementing.mp4	16.2 MB
3. While Loops and Incrementing.vtt	2.5 KB
3.1 While Loops and Incrementing - Resources.html	0.1 KB
4. Lists with the range() Function.mp4	12.0 MB
4. Lists with the range() Function.vtt	2.5 KB
4.1 Create Lists with the range() Function - Resources.html	0.1 KB
5. Lists with the range() Function.html	0.2 KB
6. Conditional Statements and Loops.mp4	16.9 MB
6. Conditional Statements and Loops.vtt	3.2 KB
6.1 Use Conditional Statements and Loops Together - Resources.html	0.1 KB
7. Conditional Statements, Functions, and Loops.mp4	10.0 MB
7. Conditional Statements, Functions, and Loops.vtt	2.1 KB
7.1 All In - Conditional Statements, Functions, and Loops - Resources.html	0.1 KB
8. How to Iterate over Dictionaries.mp4	17.8 MB
8. How to Iterate over Dictionaries.vtt	3.4 KB
8.1 Iterating over Dictionaries - Resources.html	0.1 KB
/3. The Field of Data Science - Connecting the Data Science Disciplines/
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4	133.0 MB
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt	8.1 KB
2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html	0.2 KB
/30. Python - Advanced Python Tools/
1. Object Oriented Programming.mp4	35.2 MB
1. Object Oriented Programming.vtt	5.5 KB
2. Object Oriented Programming.html	0.2 KB
3. Modules and Packages.mp4	8.9 MB
3. Modules and Packages.vtt	1.2 KB
4. Modules and Packages.html	0.2 KB
5. What is the Standard Library.mp4	18.9 MB
5. What is the Standard Library.vtt	3.2 KB
6. What is the Standard Library.html	0.2 KB
7. Importing Modules in Python.mp4	20.9 MB
7. Importing Modules in Python.vtt	4.3 KB
8. Importing Modules in Python.html	0.2 KB
/31. Part 5 Advanced Statistical Methods in Python/
1. Introduction to Regression Analysis.mp4	18.2 MB
1. Introduction to Regression Analysis.vtt	2.0 KB
2. Introduction to Regression Analysis.html	0.2 KB
/32. Advanced Statistical Methods - Linear regression with StatsModels/
1. The Linear Regression Model.mp4	60.2 MB
1. The Linear Regression Model.vtt	6.3 KB
10. Using Seaborn for Graphs.mp4	12.8 MB
10. Using Seaborn for Graphs.vtt	1.3 KB
11. How to Interpret the Regression Table.mp4	46.8 MB
11. How to Interpret the Regression Table.vtt	5.6 KB
12. How to Interpret the Regression Table.html	0.2 KB
13. Decomposition of Variability.mp4	52.1 MB
13. Decomposition of Variability.vtt	3.8 KB
14. Decomposition of Variability.html	0.2 KB
15. What is the OLS.mp4	29.7 MB
15. What is the OLS.vtt	3.4 KB
16. What is the OLS.html	0.2 KB
17. R-Squared.mp4	43.0 MB
17. R-Squared.vtt	5.9 KB
18. R-Squared.html	0.2 KB
2. The Linear Regression Model.html	0.2 KB
3. Correlation vs Regression.mp4	15.5 MB
3. Correlation vs Regression.vtt	1.9 KB
4. Correlation vs Regression.html	0.2 KB
5. Geometrical Representation of the Linear Regression Model.mp4	5.4 MB
5. Geometrical Representation of the Linear Regression Model.vtt	1.5 KB
6. Geometrical Representation of the Linear Regression Model.html	0.2 KB
7. Python Packages Installation.mp4	42.6 MB
7. Python Packages Installation.vtt	5.0 KB
8. First Regression in Python.mp4	46.7 MB
8. First Regression in Python.vtt	7.1 KB
8.1 First regression in Python.html	0.1 KB
9. First Regression in Python Exercise.html	1.4 KB
9.1 First regression in Python - Exercise.html	0.1 KB
/33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/
1. Multiple Linear Regression.mp4	22.6 MB
1. Multiple Linear Regression.vtt	3.0 KB
10. A1 Linearity.html	0.2 KB
11. A2 No Endogeneity.mp4	37.4 MB
11. A2 No Endogeneity.vtt	4.7 KB
12. A2 No Endogeneity.html	0.2 KB
13. A3 Normality and Homoscedasticity.mp4	44.8 MB
13. A3 Normality and Homoscedasticity.vtt	6.0 KB
14. A4 No Autocorrelation.mp4	33.1 MB
14. A4 No Autocorrelation.vtt	4.4 KB
15. A4 No autocorrelation.html	0.2 KB
16. A5 No Multicollinearity.mp4	30.1 MB
16. A5 No Multicollinearity.vtt	4.1 KB
17. A5 No Multicollinearity.html	0.2 KB
18. Dealing with Categorical Data - Dummy Variables.mp4	58.4 MB
18. Dealing with Categorical Data - Dummy Variables.vtt	7.3 KB
18.1 Dealing with categorical data.html	0.1 KB
19. Dealing with Categorical Data - Dummy Variables.html	0.1 KB
19.1 Dealing with categorical data.html	0.1 KB
2. Multiple Linear Regression.html	0.2 KB
20. Making Predictions with the Linear Regression.mp4	25.9 MB
20. Making Predictions with the Linear Regression.vtt	4.0 KB
20.1 Making predictions.html	0.1 KB
3. Adjusted R-Squared.mp4	57.5 MB
3. Adjusted R-Squared.vtt	6.7 KB
3.1 Adjusted R-squared.html	0.1 KB
4. Adjusted R-Squared.html	0.2 KB
5. Multiple Linear Regression Exercise.html	0.1 KB
5.1 Multiple linear regression - exercise.html	0.1 KB
6. Test for Significance of the Model (F-Test).mp4	17.2 MB
6. Test for Significance of the Model (F-Test).vtt	2.3 KB
7. OLS Assumptions.mp4	22.9 MB
7. OLS Assumptions.vtt	2.7 KB
8. OLS Assumptions.html	0.2 KB
9. A1 Linearity.mp4	13.2 MB
9. A1 Linearity.vtt	2.1 KB
/34. Advanced Statistical Methods - Linear Regression with sklearn/
1. What is sklearn and How is it Different from Other Packages.mp4	28.6 MB
1. What is sklearn and How is it Different from Other Packages.vtt	3.1 KB
10. Feature Selection (F-regression).mp4	30.9 MB
10. Feature Selection (F-regression).vtt	6.0 KB
10.1 Feature selection.html	0.1 KB
11. A Note on Calculation of P-values with sklearn.html	0.4 KB
11.1 Calculation of P-values.html	0.1 KB
12. Creating a Summary Table with p-values.mp4	12.9 MB
12. Creating a Summary Table with p-values.vtt	2.7 KB
12.1 Summary table with p-values.html	0.1 KB
13. Multiple Linear Regression - Exercise.html	0.1 KB
13.1 Multiple linear regression - Exercise.html	0.1 KB
14. Feature Scaling (Standardization).mp4	41.0 MB
14. Feature Scaling (Standardization).vtt	6.9 KB
14.1 Feature scaling.html	0.1 KB
15. Feature Selection through Standardization of Weights.mp4	36.6 MB
15. Feature Selection through Standardization of Weights.vtt	6.6 KB
15.1 Feature scaling standardization.html	0.1 KB
16. Predicting with the Standardized Coefficients.mp4	27.2 MB
16. Predicting with the Standardized Coefficients.vtt	5.0 KB
16.1 Predicting with the Standardized Cofficients.html	0.1 KB
17. Feature Scaling (Standardization) - Exercise.html	0.1 KB
17.1 Feature scaling - exercise.html	0.1 KB
18. Underfitting and Overfitting.mp4	17.8 MB
18. Underfitting and Overfitting.vtt	3.1 KB
19. Train - Test Split Explained.mp4	51.6 MB
19. Train - Test Split Explained.vtt	8.6 KB
19.1 Train - Test split explained.html	0.1 KB
2. How are Going to Approach this Section.mp4	20.4 MB
2. How are Going to Approach this Section.vtt	2.6 KB
3. Simple Linear Regression with sklearn.mp4	36.5 MB
3. Simple Linear Regression with sklearn.vtt	6.6 KB
4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4	33.6 MB
4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt	6.0 KB
5. A Note on Normalization.html	0.7 KB
6. Simple Linear Regression with sklearn - Exercise.html	0.1 KB
6.1 Simple linear regression with sklearn.html	0.1 KB
7. Multiple Linear Regression with sklearn.mp4	21.0 MB
7. Multiple Linear Regression with sklearn.vtt	3.8 KB
8. Calculating the Adjusted R-Squared in sklearn.mp4	32.4 MB
8. Calculating the Adjusted R-Squared in sklearn.vtt	5.7 KB
9. Calculating the Adjusted R-Squared in sklearn - Exercise.html	0.1 KB
9.1 Calculating the Adjusted R-Squared.html	0.1 KB
/35. Advanced Statistical Methods - Practical Example Linear Regression/
1. Practical Example Linear Regression (Part 1).mp4	101.8 MB
1. Practical Example Linear Regression (Part 1).vtt	13.3 KB
1.1 sklearn - Linear Regression - Practical Example (Part 1).html	0.1 KB
2. Practical Example Linear Regression (Part 2).mp4	48.2 MB
2. Practical Example Linear Regression (Part 2).vtt	7.2 KB
2.1 sklearn - Linear Regression - Practical Example (Part 2).html	0.1 KB
3. A Note on Multicollinearity.html	0.8 KB
4. Practical Example Linear Regression (Part 3).mp4	24.8 MB
4. Practical Example Linear Regression (Part 3).vtt	3.7 KB
4.1 sklearn - Linear Regression - Practical Example (Part 3).html	0.1 KB
5. Dummies and Variance Inflation Factor - Exercise.html	0.1 KB
5.1 Dummies and VIF - Exercise and Solution.html	0.1 KB
6. Practical Example Linear Regression (Part 4).mp4	58.8 MB
6. Practical Example Linear Regression (Part 4).vtt	10.3 KB
6.1 sklearn - Linear Regression - Practical Example (Part 4).html	0.1 KB
7. Dummy Variables - Exercise.html	0.7 KB
8. Practical Example Linear Regression (Part 5).mp4	60.7 MB
8. Practical Example Linear Regression (Part 5).vtt	9.5 KB
8.1 sklearn - Linear Regression - Practical Example (Part 5).html	0.1 KB
9. Linear Regression - Exercise.html	0.5 KB
/36. Advanced Statistical Methods - Logistic Regression/
1. Introduction to Logistic Regression.mp4	28.4 MB
1. Introduction to Logistic Regression.vtt	1.5 KB
10. Binary Predictors in a Logistic Regression.mp4	40.3 MB
10. Binary Predictors in a Logistic Regression.vtt	4.9 KB
10.1 Binary predictors.html	0.1 KB
11. Binary Predictors in a Logistic Regression - Exercise.html	0.1 KB
11.1 Binary predictors - exercise.html	0.1 KB
11.2 Bank_data.csv.csv	20.0 KB
12. Calculating the Accuracy of the Model.mp4	34.5 MB
12. Calculating the Accuracy of the Model.vtt	3.7 KB
12.1 Accuracy.html	0.1 KB
13. Calculating the Accuracy of the Model.html	0.1 KB
13.1 Accuracy of the model - exercise.html	0.1 KB
13.2 Bank_data.csv.csv	20.0 KB
14. Underfitting and Overfitting.mp4	23.4 MB
14. Underfitting and Overfitting.vtt	4.5 KB
15. Testing the Model.mp4	33.8 MB
15. Testing the Model.vtt	5.8 KB
15.1 Testing the model.html	0.1 KB
16. Testing the Model - Exercise.html	0.1 KB
16.1 Bank_data_testing.csv.csv	8.5 KB
16.2 Bank_data.csv.csv	20.0 KB
16.3 Testing the model - exercise.html	0.1 KB
2. A Simple Example in Python.mp4	36.4 MB
2. A Simple Example in Python.vtt	5.2 KB
2.1 A simple example in Python.html	0.1 KB
3. Logistic vs Logit Function.mp4	90.7 MB
3. Logistic vs Logit Function.vtt	4.4 KB
4. Building a Logistic Regression.mp4	17.9 MB
4. Building a Logistic Regression.vtt	3.0 KB
4.1 Building a logistic regression.html	0.1 KB
5. Building a Logistic Regression - Exercise.html	0.1 KB
5.1 Building a logistic regression.html	0.1 KB
5.2 Example_bank_data.csv.csv	6.4 KB
6. An Invaluable Coding Tip.mp4	24.2 MB
6. An Invaluable Coding Tip.vtt	2.8 KB
7. Understanding Logistic Regression Tables.mp4	32.0 MB
7. Understanding Logistic Regression Tables.vtt	5.0 KB
8. Understanding Logistic Regression Tables - Exercise.html	0.1 KB
8.1 Bank_data.csv.csv	20.0 KB
8.2 Understanding logistic regression.html	0.1 KB
9. What do the Odds Actually Mean.mp4	33.9 MB
9. What do the Odds Actually Mean.vtt	4.3 KB
/37. Advanced Statistical Methods - Cluster Analysis/
1. Introduction to Cluster Analysis.mp4	56.0 MB
1. Introduction to Cluster Analysis.vtt	4.3 KB
2. Some Examples of Clusters.mp4	75.0 MB
2. Some Examples of Clusters.vtt	5.6 KB
3. Difference between Classification and Clustering.mp4	37.9 MB
3. Difference between Classification and Clustering.vtt	3.0 KB
4. Math Prerequisites.mp4	15.3 MB
4. Math Prerequisites.vtt	3.6 KB
/38. Advanced Statistical Methods - K-Means Clustering/
1. K-Means Clustering.mp4	28.6 MB
1. K-Means Clustering.vtt	5.9 KB
10. Relationship between Clustering and Regression.mp4	10.4 MB
10. Relationship between Clustering and Regression.vtt	2.0 KB
11. Market Segmentation with Cluster Analysis (Part 1).mp4	45.1 MB
11. Market Segmentation with Cluster Analysis (Part 1).vtt	6.7 KB
11.1 Market segmentation.html	0.1 KB
12. Market Segmentation with Cluster Analysis (Part 2).mp4	58.8 MB
12. Market Segmentation with Cluster Analysis (Part 2).vtt	8.1 KB
12.1 Market segmentation.html	0.1 KB
13. How is Clustering Useful.mp4	78.1 MB
13. How is Clustering Useful.vtt	5.8 KB
14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html	0.1 KB
14.1 iris_dataset.csv.csv	2.5 KB
14.2 Exercise - part 1.html	0.1 KB
15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html	0.1 KB
15.1 iris_dataset.csv.csv	2.5 KB
15.2 Exercise - part 2.html	0.1 KB
15.3 iris_with_answers.csv.csv	3.7 KB
2. A Simple Example of Clustering.mp4	54.3 MB
2. A Simple Example of Clustering.vtt	8.5 KB
2.1 Example of clustering.html	0.1 KB
3. A Simple Example of Clustering - Exercise.html	0.1 KB
3.1 Countries_exercise.csv.csv	8.5 KB
3.2 A simple example of clustering.html	0.1 KB
4. Clustering Categorical Data.mp4	22.3 MB
4. Clustering Categorical Data.vtt	2.9 KB
4.1 Clustering categorical data.html	0.1 KB
5. Clustering Categorical Data - Exercise.html	0.1 KB
5.1 Categorical.csv.csv	10.6 KB
5.2 Clustering categorical data.html	0.1 KB
6. How to Choose the Number of Clusters.mp4	46.3 MB
6. How to Choose the Number of Clusters.vtt	6.6 KB
6.1 How to choose the number of clusters.html	0.1 KB
7. How to Choose the Number of Clusters - Exercise.html	0.1 KB
7.1 Countries_exercise.csv.csv	8.5 KB
7.2 How to choose the number of clusters.html	0.1 KB
8. Pros and Cons of K-Means Clustering.mp4	39.5 MB
8. Pros and Cons of K-Means Clustering.vtt	4.1 KB
9. To Standardize or not to Standardize.mp4	31.6 MB
9. To Standardize or not to Standardize.vtt	5.3 KB
/39. Advanced Statistical Methods - Other Types of Clustering/
1. Types of Clustering.mp4	46.7 MB
1. Types of Clustering.vtt	4.2 KB
2. Dendrogram.mp4	30.5 MB
2. Dendrogram.vtt	6.6 KB
3. Heatmaps.mp4	31.1 MB
3. Heatmaps.vtt	5.6 KB
3.1 Heatmaps.html	0.1 KB
/4. The Field of Data Science - The Benefits of Each Discipline/
1. The Reason behind these Disciplines.mp4	85.1 MB
1. The Reason behind these Disciplines.vtt	5.8 KB
2. The Reason behind these Disciplines.html	0.2 KB
/40. Part 6 Mathematics/
1. What is a matrix.mp4	35.2 MB
1. What is a matrix.vtt	3.9 KB
10. Addition and Subtraction of Matrices.mp4	34.2 MB
10. Addition and Subtraction of Matrices.vtt	3.6 KB
10.1 Addition and Subtraction of Matrices Python Notebook.html	0.2 KB
11. Addition and Subtraction of Matrices.html	0.2 KB
12. Errors when Adding Matrices.mp4	11.7 MB
12. Errors when Adding Matrices.vtt	2.3 KB
12.1 Errors when Adding Matrices Python Notebook.html	0.2 KB
13. Transpose of a Matrix.mp4	39.9 MB
13. Transpose of a Matrix.vtt	4.8 KB
13.1 Transpose of a Matrix Python Notebook.html	0.2 KB
14. Dot Product.mp4	25.2 MB
14. Dot Product.vtt	3.8 KB
14.1 Dot Product Python Notebook.html	0.2 KB
15. Dot Product of Matrices.mp4	51.8 MB
15. Dot Product of Matrices.vtt	8.4 KB
15.1 Dot Product of Matrices Python Notebook.html	0.2 KB
16. Why is Linear Algebra Useful.mp4	151.4 MB
16. Why is Linear Algebra Useful.vtt	10.6 KB
2. What is a Matrix.html	0.2 KB
3. Scalars and Vectors.mp4	35.5 MB
3. Scalars and Vectors.vtt	3.4 KB
4. Scalars and Vectors.html	0.2 KB
5. Linear Algebra and Geometry.mp4	52.2 MB
5. Linear Algebra and Geometry.vtt	3.6 KB
6. Linear Algebra and Geometry.html	0.2 KB
7. Arrays in Python - A Convenient Way To Represent Matrices.mp4	28.0 MB
7. Arrays in Python - A Convenient Way To Represent Matrices.vtt	5.4 KB
7.1 Arrays in Python Notebook.html	0.2 KB
8. What is a Tensor.mp4	23.6 MB
8. What is a Tensor.vtt	3.2 KB
8.1 Tensors Notebook.html	0.1 KB
9. What is a Tensor.html	0.2 KB
/41. Part 7 Deep Learning/
1. What to Expect from this Part.mp4	32.6 MB
1. What to Expect from this Part.vtt	4.1 KB
2. What is Machine Learning.html	0.2 KB
/42. Deep Learning - Introduction to Neural Networks/
1. Introduction to Neural Networks.mp4	45.0 MB
1. Introduction to Neural Networks.vtt	5.3 KB
1.1 Course Notes - Section 2.pdf.pdf	592.0 KB
10. The Linear Model with Multiple Inputs.html	0.2 KB
11. The Linear model with Multiple Inputs and Multiple Outputs.mp4	40.2 MB
11. The Linear model with Multiple Inputs and Multiple Outputs.vtt	4.9 KB
12. The Linear model with Multiple Inputs and Multiple Outputs.html	0.2 KB
13. Graphical Representation of Simple Neural Networks.mp4	23.8 MB
13. Graphical Representation of Simple Neural Networks.vtt	2.4 KB
14. Graphical Representation of Simple Neural Networks.html	0.2 KB
15. What is the Objective Function.mp4	18.8 MB
15. What is the Objective Function.vtt	1.9 KB
16. What is the Objective Function.html	0.2 KB
17. Common Objective Functions L2-norm Loss.mp4	24.4 MB
17. Common Objective Functions L2-norm Loss.vtt	2.5 KB
18. Common Objective Functions L2-norm Loss.html	0.2 KB
19. Common Objective Functions Cross-Entropy Loss.mp4	39.1 MB
19. Common Objective Functions Cross-Entropy Loss.vtt	4.7 KB
2. Introduction to Neural Networks.html	0.2 KB
20. Common Objective Functions Cross-Entropy Loss.html	0.2 KB
21. Optimization Algorithm 1-Parameter Gradient Descent.mp4	58.3 MB
21. Optimization Algorithm 1-Parameter Gradient Descent.vtt	7.6 KB
21.1 GD-function-example.xlsx.xlsx	43.4 KB
22. Optimization Algorithm 1-Parameter Gradient Descent.html	0.2 KB
23. Optimization Algorithm n-Parameter Gradient Descent.mp4	41.3 MB
23. Optimization Algorithm n-Parameter Gradient Descent.vtt	6.8 KB
24. Optimization Algorithm n-Parameter Gradient Descent.html	0.2 KB
3. Training the Model.mp4	30.1 MB
3. Training the Model.vtt	3.9 KB
3.1 Course Notes - Section 2.pdf.pdf	592.0 KB
4. Training the Model.html	0.2 KB
5. Types of Machine Learning.mp4	47.3 MB
5. Types of Machine Learning.vtt	4.7 KB
6. Types of Machine Learning.html	0.2 KB
7. The Linear Model (Linear Algebraic Version).mp4	29.8 MB
7. The Linear Model (Linear Algebraic Version).vtt	3.5 KB
8. The Linear Model.html	0.2 KB
9. The Linear Model with Multiple Inputs.mp4	26.3 MB
9. The Linear Model with Multiple Inputs.vtt	2.8 KB
/43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/
1. Basic NN Example (Part 1).mp4	21.6 MB
1. Basic NN Example (Part 1).vtt	4.0 KB
1.1 Shortcuts-for-Jupyter.pdf.pdf	634.0 KB
1.2 Bais NN Example Part 1.html	0.1 KB
2. Basic NN Example (Part 2).mp4	36.6 MB
2. Basic NN Example (Part 2).vtt	6.0 KB
2.1 Basic NN Example (Part 2).html	0.1 KB
3. Basic NN Example (Part 3).mp4	25.6 MB
3. Basic NN Example (Part 3).vtt	4.0 KB
3.1 Basic NN Example (Part 3).html	0.1 KB
4. Basic NN Example (Part 4).mp4	64.1 MB
4. Basic NN Example (Part 4).vtt	9.7 KB
4.1 Basic NN Example (Part 4).html	0.1 KB
5. Basic NN Example Exercises.html	1.4 KB
5.1 Basic NN Example Exercise 1 Solution.html	0.1 KB
5.10 Basic NN Example Exercise 4 Solution.html	0.1 KB
5.2 Basic NN Example Exercise 3c Solution.html	0.2 KB
5.3 Basic NN Example (All Exercises).html	0.1 KB
5.4 Basic NN Example Exercise 3b Solution.html	0.2 KB
5.5 Basic NN Example Exercise 3a Solution.html	0.2 KB
5.6 Basic NN Example Exercise 6 Solution.html	0.1 KB
5.7 Basic NN Example Exercise 2 Solution.html	0.1 KB
5.8 Basic NN Example Exercise 3d Solution.html	0.2 KB
5.9 Basic NN Example Exercise 5 Solution.html	0.1 KB
/44. Deep Learning - TensorFlow Introduction/
1. How to Install TensorFlow.mp4	15.3 MB
1. How to Install TensorFlow.vtt	2.9 KB
1.1 Shortcuts-for-Jupyter.pdf.pdf	634.0 KB
2. A Note on Installing Packages in Anaconda.html	2.4 KB
3. TensorFlow Outline and Logic.mp4	50.0 MB
3. TensorFlow Outline and Logic.vtt	4.7 KB
4. Actual Introduction to TensorFlow.mp4	18.3 MB
4. Actual Introduction to TensorFlow.vtt	2.0 KB
4.1 Shortcuts-for-Jupyter.pdf.pdf	634.0 KB
5. Types of File Formats, supporting Tensors.mp4	21.3 MB
5. Types of File Formats, supporting Tensors.vtt	3.1 KB
5.1 Basic NN Example with TensorFlow (Part 1).html	0.2 KB
6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4	40.4 MB
6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt	6.6 KB
6.1 Basic NN Example with TensorFlow (Part 2).html	0.2 KB
7. Basic NN Example with TF Loss Function and Gradient Descent.mp4	34.1 MB
7. Basic NN Example with TF Loss Function and Gradient Descent.vtt	4.3 KB
7.1 Basic NN Example with TensorFlow (Part 3).html	0.2 KB
8. Basic NN Example with TF Model Output.mp4	39.2 MB
8. Basic NN Example with TF Model Output.vtt	7.0 KB
8.1 Basic NN Example with TensorFlow (Complete).html	0.2 KB
9. Basic NN Example with TF Exercises.html	1.6 KB
9.1 Basic NN Example with TensorFlow Exercise 4 Solution.html	0.2 KB
9.2 Basic NN Example with TensorFlow Exercise 3 Solution.html	0.2 KB
9.3 Basic NN Example with TensorFlow Exercise 2.2 Solution.html	0.2 KB
9.4 Basic NN Example with TensorFlow Exercise 2.4 Solution.html	0.2 KB
9.5 Basic NN Example with TensorFlow Exercise 1 Solution.html	0.2 KB
9.6 Basic NN Example with TensorFlow Exercise 2.1 Solution.html	0.2 KB
9.7 Basic NN Example with TensorFlow Exercise 2.3 Solution.html	0.2 KB
9.8 Basic NN Example with TensorFlow (All Exercises).html	0.2 KB
/45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/
1. What is a Layer.mp4	13.1 MB
1. What is a Layer.vtt	2.2 KB
1.1 Course Notes - Section 6.pdf.pdf	958.9 KB
2. What is a Deep Net.mp4	31.0 MB
2. What is a Deep Net.vtt	2.9 KB
2.1 Course Notes - Section 6.pdf.pdf	958.9 KB
3. Digging into a Deep Net.mp4	62.3 MB
3. Digging into a Deep Net.vtt	6.0 KB
4. Non-Linearities and their Purpose.mp4	29.0 MB
4. Non-Linearities and their Purpose.vtt	3.5 KB
5. Activation Functions.mp4	26.3 MB
5. Activation Functions.vtt	4.7 KB
6. Activation Functions Softmax Activation.mp4	27.2 MB
6. Activation Functions Softmax Activation.vtt	4.0 KB
7. Backpropagation.mp4	36.7 MB
7. Backpropagation.vtt	4.0 KB
8. Backpropagation picture.mp4	20.4 MB
8. Backpropagation picture.vtt	3.5 KB
9. Backpropagation - A Peek into the Mathematics of Optimization.html	0.5 KB
9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf	186.7 KB
/46. Deep Learning - Overfitting/
1. What is Overfitting.mp4	32.6 MB
1. What is Overfitting.vtt	5.1 KB
2. Underfitting and Overfitting for Classification.mp4	26.3 MB
2. Underfitting and Overfitting for Classification.vtt	2.4 KB
3. What is Validation.mp4	34.3 MB
3. What is Validation.vtt	4.4 KB
4. Training, Validation, and Test Datasets.mp4	26.4 MB
4. Training, Validation, and Test Datasets.vtt	3.2 KB
5. N-Fold Cross Validation.mp4	21.7 MB
5. N-Fold Cross Validation.vtt	3.8 KB
6. Early Stopping or When to Stop Training.mp4	25.4 MB
6. Early Stopping or When to Stop Training.vtt	6.2 KB
/47. Deep Learning - Initialization/
1. What is Initialization.mp4	22.8 MB
1. What is Initialization.vtt	3.2 KB
2. Types of Simple Initializations.mp4	15.0 MB
2. Types of Simple Initializations.vtt	3.3 KB
3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4	18.0 MB
3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt	3.3 KB
/48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/
1. Stochastic Gradient Descent.mp4	30.1 MB
1. Stochastic Gradient Descent.vtt	4.3 KB
2. Problems with Gradient Descent.mp4	11.6 MB
2. Problems with Gradient Descent.vtt	2.6 KB
3. Momentum.mp4	17.2 MB
3. Momentum.vtt	3.1 KB
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4	30.5 MB
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt	5.3 KB
5. Learning Rate Schedules Visualized.mp4	9.5 MB
5. Learning Rate Schedules Visualized.vtt	1.9 KB
6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4	27.6 MB
6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt	4.7 KB
7. Adam (Adaptive Moment Estimation).mp4	23.4 MB
7. Adam (Adaptive Moment Estimation).vtt	3.0 KB
/49. Deep Learning - Preprocessing/
1. Preprocessing Introduction.mp4	29.1 MB
1. Preprocessing Introduction.vtt	3.5 KB
2. Types of Basic Preprocessing.mp4	12.4 MB
2. Types of Basic Preprocessing.vtt	1.5 KB
3. Standardization.mp4	53.5 MB
3. Standardization.vtt	5.4 KB
4. Preprocessing Categorical Data.mp4	19.5 MB
4. Preprocessing Categorical Data.vtt	2.5 KB
5. Binary and One-Hot Encoding.mp4	30.4 MB
5. Binary and One-Hot Encoding.vtt	4.3 KB
/5. The Field of Data Science - Popular Data Science Techniques/
1. Techniques for Working with Traditional Data.mp4	145.0 MB
1. Techniques for Working with Traditional Data.vtt	9.5 KB
10. Techniques for Working with Traditional Methods.mp4	129.5 MB
10. Techniques for Working with Traditional Methods.vtt	9.9 KB
11. Techniques for Working with Traditional Methods.html	0.2 KB
12. Real Life Examples of Traditional Methods.mp4	44.9 MB
12. Real Life Examples of Traditional Methods.vtt	3.2 KB
13. Machine Learning (ML) Techniques.mp4	104.2 MB
13. Machine Learning (ML) Techniques.vtt	7.9 KB
14. Machine Learning (ML) Techniques.html	0.2 KB
15. Types of Machine Learning.mp4	131.2 MB
15. Types of Machine Learning.vtt	9.5 KB
16. Types of Machine Learning.html	0.2 KB
17. Real Life Examples of Machine Learning (ML).mp4	38.6 MB
17. Real Life Examples of Machine Learning (ML).vtt	2.6 KB
18. Real Life Examples of Machine Learning (ML).html	0.2 KB
2. Techniques for Working with Traditional Data.html	0.2 KB
3. Real Life Examples of Traditional Data.mp4	31.4 MB
3. Real Life Examples of Traditional Data.vtt	2.0 KB
4. Techniques for Working with Big Data.mp4	79.2 MB
4. Techniques for Working with Big Data.vtt	5.1 KB
5. Techniques for Working with Big Data.html	0.2 KB
6. Real Life Examples of Big Data.mp4	23.1 MB
6. Real Life Examples of Big Data.vtt	1.7 KB
7. Business Intelligence (BI) Techniques.mp4	94.3 MB
7. Business Intelligence (BI) Techniques.vtt	7.7 KB
8. Business Intelligence (BI) Techniques.html	0.2 KB
9. Real Life Examples of Business Intelligence (BI).mp4	31.0 MB
9. Real Life Examples of Business Intelligence (BI).vtt	1.9 KB
/50. Deep Learning - Classifying on the MNIST Dataset/
1. MNIST What is the MNIST Dataset.mp4	18.7 MB
1. MNIST What is the MNIST Dataset.vtt	3.1 KB
10. MNIST Exercises.html	2.2 KB
10.1 TensorFlow MNIST All Exercises.html	0.1 KB
11. MNIST Solutions.html	2.2 KB
11.1 TensorFlow MNIST 'Time' Solution.html	0.2 KB
11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html	0.2 KB
11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html	0.2 KB
11.2 TensorFlow MNIST '1. Width' Solution.html	0.2 KB
11.3 TensorFlow MNIST '2. Depth' Solution.html	0.2 KB
11.4 TensorFlow MNIST '3. Width and Depth' Solution.html	0.2 KB
11.5 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html	0.2 KB
11.6 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html	0.2 KB
11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html	0.2 KB
11.8 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html	0.2 KB
11.9 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html	0.2 KB
2. MNIST How to Tackle the MNIST.mp4	23.7 MB
2. MNIST How to Tackle the MNIST.vtt	3.2 KB
3. MNIST Relevant Packages.mp4	19.8 MB
3. MNIST Relevant Packages.vtt	1.9 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.vtt	8.1 KB
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.vtt	3.2 KB
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.vtt	4.6 KB
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.vtt	2.6 KB
7.1 TensorFlow MNIST Part 5 with Comments.html	0.2 KB
8. MNIST Learning.mp4	49.0 MB
8. MNIST Learning.vtt	9.1 KB
8.1 TensorFlow MNIST Part 6 with Comments.html	0.2 KB
9. MNIST Results and Testing.mp4	65.8 MB
9. MNIST Results and Testing.vtt	7.3 KB
9.1 TensorFlow MNIST Complete Code with Comments.html	0.2 KB
/51. Deep Learning - Business Case Example/
1. Business Case Getting acquainted with the dataset.mp4	91.9 MB
1. Business Case Getting acquainted with the dataset.vtt	9.6 KB
1.1 Audiobooks_data.csv.csv	727.8 KB
10. Business Case Testing the Model.mp4	11.8 MB
10. Business Case Testing the Model.vtt	2.4 KB
11. Business Case A Comment on the Homework.mp4	38.1 MB
11. Business Case A Comment on the Homework.vtt	38.2 MB
11.1 TensorFlow Business Case Homework.html	0.1 KB
12. Business Case Final Exercise.html	0.4 KB
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.vtt	2.2 KB
3. The Importance of Working with a Balanced Dataset.mp4	41.3 MB
3. The Importance of Working with a Balanced Dataset.vtt	4.0 KB
4. Business Case Preprocessing.mp4	108.4 MB
4. Business Case Preprocessing.vtt	12.0 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.vtt	7.0 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.vtt	6.2 KB
7.1 TensorFlow Business Case Model Outline.html	0.1 KB
8. Business Case Optimization.mp4	43.5 MB
8. Business Case Optimization.vtt	5.9 KB
8.1 TensorFlow Business Case Optimization.html	0.1 KB
9. Business Case Interpretation.mp4	27.0 MB
9. Business Case Interpretation.vtt	2.7 KB
9.1 TensorFlow Business Case Interpretation.html	0.1 KB
/52. Deep Learning - Conclusion/
1. Summary on What You've Learned.mp4	41.7 MB
1. Summary on What You've Learned.vtt	4.7 KB
2. What's Further out there in terms of Machine Learning.mp4	21.1 MB
2. What's Further out there in terms of Machine Learning.vtt	2.3 KB
3. An overview of CNNs.mp4	61.6 MB
3. An overview of CNNs.vtt	5.8 KB
4. DeepMind and Deep Learning.html	1.1 KB
5. An Overview of RNNs.mp4	26.5 MB
5. An Overview of RNNs.vtt	3.4 KB
6. An Overview of non-NN Approaches.mp4	47.0 MB
6. An Overview of non-NN Approaches.vtt	4.7 KB
7. Download All Resources.html	0.5 KB
/53. Software Integration/
1. What are Data, Servers, Clients, Requests, and Responses.mp4	72.4 MB
1. What are Data, Servers, Clients, Requests, and Responses.vtt	5.3 KB
10. Software Integration - Explained.html	0.2 KB
2. What are Data, Servers, Clients, Requests, and Responses.html	0.2 KB
3. What are Data Connectivity, APIs, and Endpoints.mp4	109.1 MB
3. What are Data Connectivity, APIs, and Endpoints.vtt	7.7 KB
4. What are Data Connectivity, APIs, and Endpoints.html	0.2 KB
5. Taking a Closer Look at APIs.mp4	121.2 MB
5. Taking a Closer Look at APIs.vtt	9.4 KB
6. Taking a Closer Look at APIs.html	0.2 KB
7. Communication between Software Products through Text Files.mp4	63.3 MB
7. Communication between Software Products through Text Files.vtt	4.9 KB
8. Communication between Software Products through Text Files.html	0.2 KB
9. Software Integration - Explained.mp4	76.2 MB
9. Software Integration - Explained.vtt	6.1 KB
/54. Case Study - What's Next in the Course/
1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4	54.8 MB
1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt	4.9 KB
2. The Business Task.mp4	41.1 MB
2. The Business Task.vtt	3.4 KB
3. Introducing the Data Set.mp4	42.9 MB
3. Introducing the Data Set.vtt	3.7 KB
4. Introducing the Data Set.html	0.2 KB
/55. Case Study - Preprocessing the 'Absenteeism_data'/
1. What to Expect from the Following Sections.html	2.5 KB
1.1 data_preprocessing_homework.pdf.pdf	137.7 KB
1.2 df_preprocessed.csv.csv	29.8 KB
1.3 Absenteeism_data.csv.csv	32.8 KB
10. Analyzing the Reasons for Absence.mp4	42.5 MB
10. Analyzing the Reasons for Absence.vtt	5.2 KB
11. Obtaining Dummies from a Single Feature.mp4	85.1 MB
11. Obtaining Dummies from a Single Feature.vtt	9.2 KB
12. EXERCISE - Obtaining Dummies from a Single Feature.html	0.1 KB
13. SOLUTION - Obtaining Dummies from a Single Feature.html	0.1 KB
14. Dropping a Dummy Variable from the Data Set.html	2.4 KB
15. More on Dummy Variables A Statistical Perspective.mp4	14.4 MB
15. More on Dummy Variables A Statistical Perspective.vtt	1.5 KB
16. Classifying the Various Reasons for Absence.mp4	78.2 MB
16. Classifying the Various Reasons for Absence.vtt	9.0 KB
17. Using .concat() in Python.mp4	40.6 MB
17. Using .concat() in Python.vtt	4.5 KB
18. EXERCISE - Using .concat() in Python.html	0.2 KB
19. SOLUTION - Using .concat() in Python.html	0.1 KB
2. Importing the Absenteeism Data in Python.mp4	24.3 MB
2. Importing the Absenteeism Data in Python.vtt	3.6 KB
20. Reordering Columns in a Pandas DataFrame in Python.mp4	14.7 MB
20. Reordering Columns in a Pandas DataFrame in Python.vtt	1.6 KB
21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html	0.2 KB
22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html	0.5 KB
23. Creating Checkpoints while Coding in Jupyter.mp4	26.9 MB
23. Creating Checkpoints while Coding in Jupyter.vtt	3.3 KB
23.1 Creating Checkpoints.html	0.2 KB
24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html	0.1 KB
25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html	0.1 KB
26. Analyzing the Dates from the Initial Data Set.mp4	60.1 MB
26. Analyzing the Dates from the Initial Data Set.vtt	7.6 KB
27. Extracting the Month Value from the Date Column.mp4	50.1 MB
27. Extracting the Month Value from the Date Column.vtt	7.1 KB
28. Extracting the Day of the Week from the Date Column.mp4	29.3 MB
28. Extracting the Day of the Week from the Date Column.vtt	4.0 KB
29. EXERCISE - Removing the Date Column.html	1.2 KB
29.1 Preprocessing.html	0.2 KB
29.2 Removing the “Date” Column.html	0.2 KB
3. Checking the Content of the Data Set.mp4	64.9 MB
3. Checking the Content of the Data Set.vtt	6.3 KB
30. Analyzing Several Straightforward Columns for this Exercise.mp4	31.0 MB
30. Analyzing Several Straightforward Columns for this Exercise.vtt	3.9 KB
31. Working on Education, Children, and Pets.mp4	41.5 MB
31. Working on Education, Children, and Pets.vtt	5.1 KB
32. Final Remarks of this Section.mp4	22.7 MB
32. Final Remarks of this Section.vtt	22.7 MB
32.1 Exercises and solutions.html	0.2 KB
32.2 Preprocessing.html	0.2 KB
4. Introduction to Terms with Multiple Meanings.mp4	29.2 MB
4. Introduction to Terms with Multiple Meanings.vtt	3.7 KB
5. What's Regression Analysis - a Quick Refresher.html	2.9 KB
6. Using a Statistical Approach towards the Solution to the Exercise.mp4	21.2 MB
6. Using a Statistical Approach towards the Solution to the Exercise.vtt	2.5 KB
7. Dropping a Column from a DataFrame in Python.mp4	64.8 MB
7. Dropping a Column from a DataFrame in Python.vtt	7.0 KB
8. EXERCISE - Dropping a Column from a DataFrame in Python.html	0.9 KB
9. SOLUTION - Dropping a Column from a DataFrame in Python.html	0.1 KB
/56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/
1. Exploring the Problem with a Machine Learning Mindset.mp4	28.9 MB
1. Exploring the Problem with a Machine Learning Mindset.vtt	4.1 KB
1.1 Absenteeism_preprocessed.csv.csv	29.8 KB
10. Interpreting the Coefficients of the Logistic Regression.mp4	42.4 MB
10. Interpreting the Coefficients of the Logistic Regression.vtt	6.5 KB
11. Backward Elimination or How to Simplify Your Model.mp4	41.5 MB
11. Backward Elimination or How to Simplify Your Model.vtt	4.7 KB
11.1 Logistic Regression prior to Backward Elimination.html	0.2 KB
12. Testing the Model We Created.mp4	51.5 MB
12. Testing the Model We Created.vtt	5.8 KB
13. Saving the Model and Preparing it for Deployment.mp4	39.3 MB
13. Saving the Model and Preparing it for Deployment.vtt	5.0 KB
14. ARTICLE - A Note on 'pickling'.html	2.2 KB
15. EXERCISE - Saving the Model (and Scaler).html	0.3 KB
15.1 Logistic Regression.html	0.2 KB
15.2 Logistic Regression with Comments.html	0.2 KB
16. Preparing the Deployment of the Model through a Module.mp4	46.6 MB
16. Preparing the Deployment of the Model through a Module.vtt	5.0 KB
2. Creating the Targets for the Logistic Regression.mp4	48.0 MB
2. Creating the Targets for the Logistic Regression.vtt	7.5 KB
3. Selecting the Inputs for the Logistic Regression.mp4	17.6 MB
3. Selecting the Inputs for the Logistic Regression.vtt	3.2 KB
4. Standardizing the Data.mp4	21.6 MB
4. Standardizing the Data.vtt	3.8 KB
5. Splitting the Data for Training and Testing.mp4	55.3 MB
5. Splitting the Data for Training and Testing.vtt	7.2 KB
6. Fitting the Model and Assessing its Accuracy.mp4	43.7 MB
6. Fitting the Model and Assessing its Accuracy.vtt	6.6 KB
7. Creating a Summary Table with the Coefficients and Intercept.mp4	40.8 MB
7. Creating a Summary Table with the Coefficients and Intercept.vtt	5.9 KB
8. Interpreting the Coefficients for Our Problem.mp4	54.9 MB
8. Interpreting the Coefficients for Our Problem.vtt	7.1 KB
9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4	43.2 MB
9. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt	4.5 KB
9.1 Logistic Regression prior to Custom Scaler.html	0.2 KB
/57. Case Study - Loading the 'absenteeism_module'/
1. Are You Sure You're All Set.html	0.5 KB
1.1 5 Files Needed to Deploy the Model.html	0.1 KB
2. Deploying the 'absenteeism_module' - Part I.mp4	26.7 MB
2. Deploying the 'absenteeism_module' - Part I.vtt	4.3 KB
3. Deploying the 'absenteeism_module' - Part II.mp4	56.9 MB
3. Deploying the 'absenteeism_module' - Part II.vtt	6.8 KB
4. Exporting the Obtained Data Set as a .csv.html	1.0 KB
4.1 Deploying the ‘absenteeism_module.html	0.2 KB
/58. Case Study - Analyzing the Predicted Outputs in Tableau/
1. EXERCISE - Age vs Probability.html	0.4 KB
2. Analyzing Age vs Probability in Tableau.mp4	59.3 MB
2. Analyzing Age vs Probability in Tableau.vtt	9.0 KB
3. EXERCISE - Reasons vs Probability.html	0.4 KB
4. Analyzing Reasons vs Probability in Tableau.mp4	62.2 MB
4. Analyzing Reasons vs Probability in Tableau.vtt	8.6 KB
5. EXERCISE - Transportation Expense vs Probability.html	0.6 KB
6. Analyzing Transportation Expense vs Probability in Tableau.mp4	42.6 MB
6. Analyzing Transportation Expense vs Probability in Tableau.vtt	6.5 KB
/6. The Field of Data Science - Popular Data Science Tools/
1. Necessary Programming Languages and Software Used in Data Science.mp4	108.6 MB
1. Necessary Programming Languages and Software Used in Data Science.vtt	6.6 KB
2. Necessary Programming Languages and Software Used in Data Science.html	0.2 KB
/7. The Field of Data Science - Careers in Data Science/
1. Finding the Job - What to Expect and What to Look for.mp4	57.0 MB
1. Finding the Job - What to Expect and What to Look for.vtt	4.0 KB
2. Finding the Job - What to Expect and What to Look for.html	0.2 KB
/8. The Field of Data Science - Debunking Common Misconceptions/
1. Debunking Common Misconceptions.mp4	76.4 MB
1. Debunking Common Misconceptions.vtt	4.8 KB
2. Debunking Common Misconceptions.html	0.2 KB
/9. Part 2 Probability/
1. The Basic Probability Formula.mp4	90.1 MB
1. The Basic Probability Formula.vtt	8.0 KB
1.1 Course Notes - Basic Probability.pdf.pdf	380.0 KB
2. The Basic Probability Formula.html	0.2 KB
3. Computing Expected Values.mp4	79.4 MB
3. Computing Expected Values.vtt	6.0 KB
4. Computing Expected Values.html	0.2 KB
5. Frequency.mp4	64.7 MB
5. Frequency.vtt	5.8 KB
6. Frequency.html	0.2 KB
7. Events and Their Complements.mp4	62.0 MB
7. Events and Their Complements.vtt	6.1 KB
8. Events and Their Complements.html	0.2 KB
/
[FreeCourseLab.com].url	0.1 KB
Total files 1226