Udemy The Data Science Course Complete Data Science Bootcamp 2025 12 2024

Udemy The Data Science Course Complete Data Science Bootcamp 2025 12 2024

Name	Udemy - The Data Science Course Complete Data Science Bootcamp 2025 (12.2024)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	9.8 GB
Total Files	1506
Last Seen	2025-07-19 00:12
Hash	3B3FAA780C8E881B100CF6B1CE304E18ED36BB65

/assets/
03. FAQ-The-Data-Science-Course.pdf	313.4 KB
/external-links/
03. Download-all-resources.url	0.1 KB
/01. Part 1 Introduction/
01. A Practical Example What You Will Learn in This Course.mp4	11.3 MB
02. What Does the Course Cover.mp4	10.0 MB
03. Download All Resources and Important FAQ.html	21.9 KB
01. A Practical Example What You Will Learn in This Course.vtt	7.0 KB
02. What Does the Course Cover.vtt	5.6 KB
/assets/
04. 365-DataScience.png	7.3 MB
07. 365-DataScience.png	7.3 MB
04. 365-DataScience-Diagram.pdf	330.8 KB
03. 365-DataScience-Diagram.pdf	330.8 KB
/external-links/
02. Intro-to-Data-Science-Flashcards.url	0.0 KB
01. Intro-to-Data-Science-Flashcards.url	0.0 KB
/02. The Field of Data Science - The Various Data Science Disciplines/
04. Continuing with BI, ML, and AI.mp4	49.9 MB
07. A Breakdown of our Data Science Infographic.mp4	47.6 MB
06. More Examples of Generative AI.mp4	32.0 MB
05. Traditional AI vs. Generative AI.mp4	25.7 MB
01. Data Science and Business Buzzwords Why are there so Many.mp4	16.3 MB
03. Business Analytics, Data Analytics, and Data Science An Introduction.mp4	15.3 MB
02. What is the difference between Analysis and Analytics.mp4	11.7 MB
04. Continuing with BI, ML, and AI.vtt	13.4 KB
03. Business Analytics, Data Analytics, and Data Science An Introduction.vtt	9.9 KB
01. Data Science and Business Buzzwords Why are there so Many.vtt	7.5 KB
05. Traditional AI vs. Generative AI.vtt	7.1 KB
06. More Examples of Generative AI.vtt	7.0 KB
02. What is the difference between Analysis and Analytics.vtt	5.2 KB
07. A Breakdown of our Data Science Infographic.vtt	5.2 KB
/03. The Field of Data Science - Connecting the Data Science Disciplines/
01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4	87.6 MB
01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt	9.5 KB
/04. The Field of Data Science - The Benefits of Each Discipline/
01. The Reason Behind These Disciplines.mp4	49.0 MB
01. The Reason Behind These Disciplines.vtt	6.7 KB
/05. The Field of Data Science - Popular Data Science Techniques/
01. Techniques for Working with Traditional Data.mp4	112.4 MB
07. Techniques for Working with Traditional Methods.mp4	79.7 MB
10. Types of Machine Learning.mp4	72.8 MB
03. Techniques for Working with Big Data.mp4	65.1 MB
05. Business Intelligence (BI) Techniques.mp4	55.5 MB
09. Machine Learning (ML) Techniques.mp4	51.8 MB
08. Real Life Examples of Traditional Methods.mp4	38.5 MB
12. Real Life Examples of Machine Learning (ML).mp4	29.1 MB
11. Evolution and Latest Trends of Machine Learning (ML).mp4	28.7 MB
06. Real Life Examples of Business Intelligence (BI).mp4	25.8 MB
02. Real Life Examples of Traditional Data.mp4	19.3 MB
04. Real Life Examples of Big Data.mp4	13.7 MB
07. Techniques for Working with Traditional Methods.vtt	11.8 KB
01. Techniques for Working with Traditional Data.vtt	11.2 KB
10. Types of Machine Learning.vtt	11.2 KB
09. Machine Learning (ML) Techniques.vtt	9.5 KB
05. Business Intelligence (BI) Techniques.vtt	9.0 KB
11. Evolution and Latest Trends of Machine Learning (ML).vtt	7.9 KB
03. Techniques for Working with Big Data.vtt	6.0 KB
08. Real Life Examples of Traditional Methods.vtt	5.5 KB
12. Real Life Examples of Machine Learning (ML).vtt	3.1 KB
02. Real Life Examples of Traditional Data.vtt	2.4 KB
06. Real Life Examples of Business Intelligence (BI).vtt	2.3 KB
04. Real Life Examples of Big Data.vtt	1.9 KB
/06. The Field of Data Science - Popular Data Science Tools/
01. Necessary Programming Languages and Software Used in Data Science.mp4	86.4 MB
01. Necessary Programming Languages and Software Used in Data Science.vtt	8.2 KB
/07. The Field of Data Science - Careers in Data Science/
01. Finding the Job - What to Expect and What to Look for.mp4	42.0 MB
01. Finding the Job - What to Expect and What to Look for.vtt	4.8 KB
/08. The Field of Data Science - Debunking Common Misconceptions/
01. Debunking Common Misconceptions.mp4	61.7 MB
01. Debunking Common Misconceptions.vtt	5.7 KB
/assets/
01. Course-Notes-Basic-Probability.pdf	380.0 KB
/external-links/
01. Probability-Flashcards.url	0.0 KB
/09. Part 2 Probability/
02. Computing Expected Values.mp4	47.9 MB
03. Frequency.mp4	39.2 MB
01. The Basic Probability Formula.mp4	30.8 MB
04. Events and Their Complements.mp4	27.1 MB
01. The Basic Probability Formula.vtt	9.2 KB
04. Events and Their Complements.vtt	7.3 KB
02. Computing Expected Values.vtt	7.1 KB
03. Frequency.vtt	7.1 KB
/assets/
11. Additional-Exercises-Combinatorics-Solutions.pdf	251.6 KB
01. Course-Notes-Combinatorics.pdf	231.5 KB
06. Combinations-With-Repetition.pdf	212.4 KB
11. Additional-Exercises-Combinatorics.pdf	109.1 KB
07. Symmetry-Explained.pdf	87.1 KB
/10. Probability - Combinatorics/
11. A Practical Example of Combinatorics.mp4	84.6 MB
06. Solving Combinations.mp4	24.8 MB
08. Solving Combinations with Separate Sample Spaces.mp4	21.3 MB
05. Solving Variations without Repetition.mp4	19.1 MB
02. Permutations and How to Use Them.mp4	18.4 MB
09. Combinatorics in Real-Life The Lottery.mp4	17.2 MB
04. Solving Variations with Repetition.mp4	14.6 MB
07. Symmetry of Combinations.mp4	14.4 MB
10. A Recap of Combinatorics.mp4	12.7 MB
03. Simple Operations with Factorials.mp4	11.0 MB
01. Fundamentals of Combinatorics.mp4	6.2 MB
11. A Practical Example of Combinatorics.vtt	15.5 KB
06. Solving Combinations.vtt	6.2 KB
05. Solving Variations without Repetition.vtt	5.1 KB
07. Symmetry of Combinations.vtt	4.6 KB
02. Permutations and How to Use Them.vtt	4.5 KB
09. Combinatorics in Real-Life The Lottery.vtt	4.3 KB
08. Solving Combinations with Separate Sample Spaces.vtt	4.2 KB
10. A Recap of Combinatorics.vtt	3.9 KB
04. Solving Variations with Repetition.vtt	3.8 KB
03. Simple Operations with Factorials.vtt	3.5 KB
01. Fundamentals of Combinatorics.vtt	1.5 KB
/assets/
12. CDS-2017-2018-Hamilton.pdf	865.6 KB
01. Course-Notes-Bayesian-Inference.pdf	395.3 KB
12. Bayesian-Homework-Solutions.pdf	31.1 KB
12. Bayesian-Homework.pdf	27.9 KB
/11. Probability - Bayesian Inference/
12. A Practical Example of Bayesian Inference.mp4	146.0 MB
04. Union of Sets.mp4	25.4 MB
11. Bayes' Law.mp4	22.4 MB
10. The Multiplication Law.mp4	21.2 MB
07. The Conditional Probability Formula.mp4	21.0 MB
01. Sets and Events.mp4	18.5 MB
06. Dependence and Independence of Sets.mp4	15.6 MB
08. The Law of Total Probability.mp4	14.9 MB
02. Ways Sets Can Interact.mp4	11.9 MB
09. The Additive Rule.mp4	11.6 MB
03. Intersection of Sets.mp4	11.6 MB
05. Mutually Exclusive Sets.mp4	11.1 MB
12. A Practical Example of Bayesian Inference.vtt	20.6 KB
11. Bayes' Law.vtt	7.9 KB
04. Union of Sets.vtt	6.4 KB
07. The Conditional Probability Formula.vtt	6.0 KB
01. Sets and Events.vtt	5.6 KB
10. The Multiplication Law.vtt	4.8 KB
02. Ways Sets Can Interact.vtt	4.7 KB
08. The Law of Total Probability.vtt	4.0 KB
06. Dependence and Independence of Sets.vtt	3.6 KB
05. Mutually Exclusive Sets.vtt	2.8 KB
09. The Additive Rule.vtt	2.7 KB
03. Intersection of Sets.vtt	2.7 KB
/assets/
15. FIFA19-post.csv	9.1 MB
15. FIFA19.csv	9.1 MB
01. Course-Notes-Probability-Distributions.pdf	475.1 KB
08. Solving-Integrals.pdf	352.1 KB
07. Poisson-Expected-Value-and-Variance.pdf	149.5 KB
09. Normal-Distribution-Exp-and-Var.pdf	147.5 KB
15. Daily-Views-post.xlsx	20.7 KB
15. Customers-Membership-post.xlsx	16.0 KB
15. Customers-Membership.xlsx	9.9 KB
15. Daily-Views.xlsx	9.8 KB
/12. Probability - Distributions/
15. A Practical Example of Probability Distributions.mp4	145.0 MB
02. Types of Probability Distributions.mp4	37.3 MB
06. Discrete Distributions The Binomial Distribution.mp4	32.1 MB
07. Discrete Distributions The Poisson Distribution.mp4	25.1 MB
08. Characteristics of Continuous Distributions.mp4	22.3 MB
10. Continuous Distributions The Standard Normal Distribution.mp4	22.1 MB
09. Continuous Distributions The Normal Distribution.mp4	21.0 MB
01. Fundamentals of Probability Distributions.mp4	20.4 MB
14. Continuous Distributions The Logistic Distribution.mp4	17.0 MB
13. Continuous Distributions The Exponential Distribution.mp4	16.8 MB
05. Discrete Distributions The Bernoulli Distribution.mp4	15.9 MB
12. Continuous Distributions The Chi-Squared Distribution.mp4	11.7 MB
04. Discrete Distributions The Uniform Distribution.mp4	10.8 MB
03. Characteristics of Discrete Distributions.mp4	9.9 MB
11. Continuous Distributions The Students' T Distribution.mp4	9.7 MB
15. A Practical Example of Probability Distributions.vtt	21.6 KB
02. Types of Probability Distributions.vtt	10.7 KB
08. Characteristics of Continuous Distributions.vtt	9.4 KB
06. Discrete Distributions The Binomial Distribution.vtt	9.0 KB
01. Fundamentals of Probability Distributions.vtt	8.6 KB
07. Discrete Distributions The Poisson Distribution.vtt	7.4 KB
10. Continuous Distributions The Standard Normal Distribution.vtt	5.9 KB
14. Continuous Distributions The Logistic Distribution.vtt	5.5 KB
05. Discrete Distributions The Bernoulli Distribution.vtt	5.3 KB
09. Continuous Distributions The Normal Distribution.vtt	5.2 KB
13. Continuous Distributions The Exponential Distribution.vtt	4.6 KB
11. Continuous Distributions The Students' T Distribution.vtt	3.3 KB
12. Continuous Distributions The Chi-Squared Distribution.vtt	3.1 KB
04. Discrete Distributions The Uniform Distribution.vtt	3.0 KB
03. Characteristics of Discrete Distributions.vtt	2.6 KB
/assets/
03. Probability-Cheat-Sheet.pdf	328.0 KB
01. Probability-in-Finance-Solutions.pdf	188.9 KB
01. Probability-in-Finance-Homework.pdf	113.3 KB
/13. Probability - Probability in Other Fields/
01. Probability in Finance.mp4	42.3 MB
02. Probability in Statistics.mp4	33.1 MB
03. Probability in Data Science.mp4	14.9 MB
01. Probability in Finance.vtt	10.3 KB
02. Probability in Statistics.vtt	9.3 KB
03. Probability in Data Science.vtt	7.3 KB
/assets/
01. Course-notes-descriptive-statistics.pdf	493.8 KB
01. Statistics-Glossary.xlsx	20.8 KB
/external-links/
01. Statistics-Flashcards.url	0.1 KB
/14. Part 3 Statistics/
01. Population and Sample.mp4	36.8 MB
01. Population and Sample.vtt	6.0 KB
/assets/
01. Course-notes-descriptive-statistics.pdf	493.8 KB
04. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf	296.1 KB
08. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf	296.1 KB
04. 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx	42.1 KB
10. 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx	41.4 KB
13. 2.8.Skewness-lesson.xlsx	35.5 KB
03. 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx	31.5 KB
20. 2.11.Covariance-exercise-solution.xlsx	30.2 KB
22. 2.12.Correlation-exercise-solution.xlsx	30.2 KB
22. 2.12.Correlation-exercise.xlsx	30.0 KB
09. 2.6.Cross-table-and-scatter-plot.xlsx	26.7 KB
19. 2.11.Covariance-lesson.xlsx	25.5 KB
20. 2.11.Covariance-exercise.xlsx	20.7 KB
01. Glossary.xlsx	20.4 KB
14. 2.8.Skewness-exercise-solution.xlsx	20.3 KB
07. 2.5.The-Histogram-lesson.xlsx	19.1 KB
08. 2.5.The-Histogram-exercise-solution.xlsx	17.5 KB
10. 2.6.Cross-table-and-scatter-plot-exercise.xlsx	16.7 KB
08. 2.5.The-Histogram-exercise.xlsx	15.9 KB
04. 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx	15.6 KB
06. 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx	13.5 KB
18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx	12.9 KB
18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx	11.9 KB
05. 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx	11.7 KB
12. 2.7.Mean-median-and-mode-exercise-solution.xlsx	11.6 KB
16. 2.9.Variance-exercise-solution.xlsx	11.3 KB
17. 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx	11.2 KB
12. 2.7.Mean-median-and-mode-exercise.xlsx	11.1 KB
16. 2.9.Variance-exercise.xlsx	11.1 KB
11. 2.7.Mean-median-and-mode-lesson.xlsx	10.7 KB
15. 2.9.Variance-lesson.xlsx	10.3 KB
14. 2.8.Skewness-exercise.xlsx	9.7 KB
/15. Statistics - Descriptive Statistics/
01. Types of Data.mp4	45.3 MB
02. Levels of Measurement.mp4	33.8 MB
03. Categorical Variables - Visualization Techniques.mp4	28.8 MB
11. Mean, median and mode.mp4	25.7 MB
15. Variance.mp4	24.7 MB
17. Standard Deviation and Coefficient of Variation.mp4	21.1 MB
09. Cross Tables and Scatter Plots.mp4	20.7 MB
21. Correlation Coefficient.mp4	20.3 MB
19. Covariance.mp4	19.3 MB
05. Numerical Variables - Frequency Distribution Table.mp4	18.6 MB
13. Skewness.mp4	14.0 MB
07. The Histogram.mp4	10.0 MB
15. Variance.vtt	8.4 KB
09. Cross Tables and Scatter Plots.vtt	7.1 KB
03. Categorical Variables - Visualization Techniques.vtt	6.9 KB
17. Standard Deviation and Coefficient of Variation.vtt	6.5 KB
11. Mean, median and mode.vtt	6.1 KB
01. Types of Data.vtt	6.0 KB
19. Covariance.vtt	5.2 KB
21. Correlation Coefficient.vtt	5.1 KB
02. Levels of Measurement.vtt	4.9 KB
05. Numerical Variables - Frequency Distribution Table.vtt	4.6 KB
13. Skewness.vtt	3.8 KB
07. The Histogram.vtt	3.4 KB
16. Variance Exercise.html	0.5 KB
04. Categorical Variables Exercise.html	0.1 KB
18. Standard Deviation and Coefficient of Variation Exercise.html	0.1 KB
06. Numerical Variables Exercise.html	0.1 KB
10. Cross Tables and Scatter Plots Exercise.html	0.1 KB
14. Skewness Exercise.html	0.1 KB
20. Covariance Exercise.html	0.1 KB
08. Histogram Exercise.html	0.1 KB
12. Mean, Median and Mode Exercise.html	0.1 KB
22. Correlation Coefficient Exercise.html	0.1 KB
/assets/
01. 2.13.Practical-example.Descriptive-statistics-lesson.xlsx	150.0 KB
02. 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx	149.9 KB
02. 2.13.Practical-example.Descriptive-statistics-exercise.xlsx	123.2 KB
/16. Statistics - Practical Example Descriptive Statistics/
01. Practical Example Descriptive Statistics.mp4	136.9 MB
01. Practical Example Descriptive Statistics.vtt	21.5 KB
02. Practical Example Descriptive Statistics Exercise.html	0.1 KB
/assets/
02. Course-notes-inferential-statistics.pdf	391.5 KB
01. Course-notes-inferential-statistics.pdf	391.5 KB
05. 3.4.Standard-normal-distribution-exercise-solution.xlsx	24.6 KB
02. 3.2.What-is-a-distribution-lesson.xlsx	19.9 KB
05. 3.4.Standard-normal-distribution-exercise.xlsx	12.3 KB
04. 3.4.Standard-normal-distribution-lesson.xlsx	10.6 KB
/17. Statistics - Inferential Statistics Fundamentals/
08. Estimators and Estimates.mp4	29.0 MB
06. Central Limit Theorem.mp4	24.3 MB
02. What is a Distribution.mp4	18.0 MB
07. Standard error.mp4	14.2 MB
03. The Normal Distribution.mp4	13.7 MB
04. The Standard Normal Distribution.mp4	9.0 MB
01. Introduction.mp4	3.2 MB
02. What is a Distribution.vtt	6.0 KB
06. Central Limit Theorem.vtt	5.7 KB
03. The Normal Distribution.vtt	5.3 KB
04. The Standard Normal Distribution.vtt	4.1 KB
08. Estimators and Estimates.vtt	4.1 KB
07. Standard error.vtt	2.1 KB
01. Introduction.vtt	1.7 KB
05. The Standard Normal Distribution Exercise.html	0.1 KB
/assets/
02. 3.9.The-z-table.xlsx	26.2 KB
03. 3.9.The-z-table.xlsx	26.2 KB
07. 3.11.The-t-table.xlsx	16.2 KB
06. 3.11.The-t-table.xlsx	16.2 KB
10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx	14.6 KB
10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx	14.1 KB
02. 3.9.Population-variance-known-z-score-lesson.xlsx	11.5 KB
03. 3.9.Population-variance-known-z-score-exercise-solution.xlsx	11.4 KB
07. 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx	11.4 KB
03. 3.9.Population-variance-known-z-score-exercise.xlsx	11.1 KB
06. 3.11.Population-variance-unknown-t-score-lesson.xlsx	11.0 KB
07. 3.11.Population-variance-unknown-t-score-exercise.xlsx	10.9 KB
09. 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx	10.7 KB
12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx	10.4 KB
11. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx	10.1 KB
12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx	10.1 KB
14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx	10.0 KB
13. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx	9.7 KB
14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx	9.4 KB
/18. Statistics - Inferential Statistics Confidence Intervals/
02. Confidence Intervals; Population Variance Known; Z-score.mp4	54.7 MB
09. Confidence intervals. Two means. Dependent samples.mp4	47.2 MB
01. What are Confidence Intervals.mp4	30.0 MB
08. Margin of Error.mp4	24.2 MB
04. Confidence Interval Clarifications.mp4	19.9 MB
13. Confidence intervals. Two means. Independent Samples (Part 2).mp4	15.3 MB
06. Confidence Intervals; Population Variance Unknown; T-score.mp4	14.4 MB
05. Student's T Distribution.mp4	14.3 MB
11. Confidence intervals. Two means. Independent Samples (Part 1).mp4	12.6 MB
15. Confidence intervals. Two means. Independent Samples (Part 3).mp4	7.2 MB
02. Confidence Intervals; Population Variance Known; Z-score.vtt	9.8 KB
09. Confidence intervals. Two means. Dependent samples.vtt	8.7 KB
08. Margin of Error.vtt	6.6 KB
11. Confidence intervals. Two means. Independent Samples (Part 1).vtt	6.4 KB
04. Confidence Interval Clarifications.vtt	5.7 KB
06. Confidence Intervals; Population Variance Unknown; T-score.vtt	5.5 KB
13. Confidence intervals. Two means. Independent Samples (Part 2).vtt	4.8 KB
05. Student's T Distribution.vtt	4.7 KB
01. What are Confidence Intervals.vtt	3.3 KB
15. Confidence intervals. Two means. Independent Samples (Part 3).vtt	2.1 KB
03. Confidence Intervals; Population Variance Known; Z-score; Exercise.html	0.1 KB
14. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html	0.1 KB
07. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html	0.1 KB
10. Confidence intervals. Two means. Dependent samples Exercise.html	0.1 KB
12. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html	0.1 KB
/assets/
02. 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx	1.9 MB
01. 3.17.Practical-example.Confidence-intervals-lesson.xlsx	1.8 MB
02. 3.17.Practical-example.Confidence-intervals-exercise.xlsx	1.8 MB
/19. Statistics - Practical Example Inferential Statistics/
01. Practical Example Inferential Statistics.mp4	72.4 MB
01. Practical Example Inferential Statistics.vtt	14.2 KB
02. Practical Example Inferential Statistics Exercise.html	0.1 KB
/assets/
07. Online-p-value-calculator.pdf	1.2 MB
03. Course-notes-hypothesis-testing.pdf	672.2 KB
01. Course-notes-hypothesis-testing.pdf	672.2 KB
08. 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx	14.9 KB
11. 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx	14.7 KB
11. 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx	13.1 KB
09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx	12.9 KB
15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx	11.7 KB
09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx	11.6 KB
13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx	11.5 KB
06. 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx	11.5 KB
06. 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx	11.3 KB
05. 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx	11.2 KB
13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx	11.0 KB
15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx	10.8 KB
10. 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx	10.0 KB
12. 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx	9.9 KB
14. 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx	9.5 KB
/20. Statistics - Hypothesis Testing/
03. Rejection Region and Significance Level.mp4	40.6 MB
05. Test for the Mean. Population Variance Known.mp4	38.7 MB
07. p-value.mp4	35.4 MB
10. Test for the Mean. Dependent Samples.mp4	34.4 MB
01. Null vs Alternative Hypothesis.mp4	33.5 MB
14. Test for the mean. Independent Samples (Part 2).mp4	25.6 MB
08. Test for the Mean. Population Variance Unknown.mp4	20.7 MB
12. Test for the mean. Independent Samples (Part 1).mp4	16.2 MB
04. Type I Error and Type II Error.mp4	16.0 MB
03. Rejection Region and Significance Level.vtt	8.8 KB
05. Test for the Mean. Population Variance Known.vtt	8.3 KB
01. Null vs Alternative Hypothesis.vtt	7.3 KB
10. Test for the Mean. Dependent Samples.vtt	6.9 KB
08. Test for the Mean. Population Variance Unknown.vtt	6.1 KB
04. Type I Error and Type II Error.vtt	5.6 KB
12. Test for the mean. Independent Samples (Part 1).vtt	5.6 KB
14. Test for the mean. Independent Samples (Part 2).vtt	5.5 KB
07. p-value.vtt	5.4 KB
02. Further Reading on Null and Alternative Hypothesis.html	2.3 KB
13. Test for the mean. Independent Samples (Part 1). Exercise.html	0.1 KB
09. Test for the Mean. Population Variance Unknown Exercise.html	0.1 KB
06. Test for the Mean. Population Variance Known Exercise.html	0.1 KB
15. Test for the mean. Independent Samples (Part 2). Exercise.html	0.1 KB
11. Test for the Mean. Dependent Samples Exercise.html	0.1 KB
/assets/
01. 4.10.Hypothesis-testing-section-practical-example.xlsx	53.1 KB
02. 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx	45.3 KB
02. 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx	44.7 KB
/21. Statistics - Practical Example Hypothesis Testing/
01. Practical Example Hypothesis Testing.mp4	48.1 MB
01. Practical Example Hypothesis Testing.vtt	8.8 KB
02. Practical Example Hypothesis Testing Exercise.html	0.1 KB
/assets/
01. Introduction-to-Python-Course-Notes.pdf	2.3 MB
/external-links/
01. Intro-to-Python-Flashcards.url	0.0 KB
/22. Part 4 Introduction to Python/
06. Prerequisites for Coding in the Jupyter Notebooks.mp4	19.9 MB
04. Installing Python and Jupyter.mp4	19.7 MB
01. Introduction to Programming.mp4	15.6 MB
02. Why Python.mp4	12.8 MB
03. Why Jupyter.mp4	8.4 MB
05. Understanding Jupyter's Interface - the Notebook Dashboard.mp4	6.4 MB
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02. Why Python.vtt	7.3 KB
04. Installing Python and Jupyter.vtt	5.0 KB
03. Why Jupyter.vtt	4.3 KB
05. Understanding Jupyter's Interface - the Notebook Dashboard.vtt	3.8 KB
/assets/
01. Introduction-to-Python-Course-Notes.pdf	2.3 MB
03. Strings-Lecture-Py3.ipynb	7.7 KB
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01. Variables-Solution-Py3.ipynb	3.9 KB
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02. Numbers-and-Boolean-Values-Lecture-Py3.ipynb	3.4 KB
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02. Numbers-and-Boolean-Values-Exercise-Py3.ipynb	2.3 KB
01. Variables-Exercise-Py3.ipynb	2.3 KB
/23. Python - Variables and Data Types/
03. Python Strings.mp4	20.7 MB
01. Variables.mp4	9.4 MB
02. Numbers and Boolean Values in Python.mp4	6.9 MB
03. Python Strings.vtt	7.8 KB
01. Variables.vtt	4.9 KB
02. Numbers and Boolean Values in Python.vtt	3.8 KB
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01. Arithmetic-Operators-Solution-Py3.ipynb	4.3 KB
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02. The-Double-Equality-Sign-Exercise-Py3.ipynb	0.8 KB
05. Line-Continuation-Lecture-Py3.ipynb	0.8 KB
/24. Python - Basic Python Syntax/
01. Using Arithmetic Operators in Python.mp4	9.0 MB
07. Structuring with Indentation.mp4	2.9 MB
02. The Double Equality Sign.mp4	2.8 MB
04. Add Comments.mp4	2.5 MB
06. Indexing Elements.mp4	2.5 MB
03. How to Reassign Values.mp4	2.0 MB
05. Understanding Line Continuation.mp4	1.3 MB
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06. Indexing Elements.vtt	1.7 KB
03. How to Reassign Values.vtt	1.4 KB
05. Understanding Line Continuation.vtt	1.2 KB
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02. Logical-and-Identity-Operators-Lecture-Py3.ipynb	6.0 KB
02. Logical-and-Identity-Operators-Solution-Py3.ipynb	3.5 KB
01. Comparison-Operators-Lecture-Py3.ipynb	2.6 KB
01. Comparison-Operators-Solution-Py3.ipynb	2.5 KB
01. Comparison-Operators-Exercise-Py3.ipynb	1.6 KB
/25. Python - Other Python Operators/
02. Logical and Identity Operators.mp4	19.9 MB
01. Comparison Operators.mp4	4.4 MB
02. Logical and Identity Operators.vtt	6.1 KB
01. Comparison Operators.vtt	2.6 KB
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03. Else-If-for-Brief-Elif-Lecture-Py3.ipynb	3.3 KB
03. Else-If-for-Brief-Elif-Solution-Py3.ipynb	2.5 KB
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02. Add-an-Else-Statement-Exercise-Py3.ipynb	1.0 KB
04. A-Note-on-Boolean-Values-Lecture-Py3.ipynb	0.8 KB
/26. Python - Conditional Statements/
03. The ELIF Statement.mp4	14.9 MB
01. The IF Statement.mp4	7.0 MB
02. The ELSE Statement.mp4	6.3 MB
04. A Note on Boolean Values.mp4	4.4 MB
03. The ELIF Statement.vtt	7.0 KB
01. The IF Statement.vtt	3.8 KB
02. The ELSE Statement.vtt	3.3 KB
04. A Note on Boolean Values.vtt	3.2 KB
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07. Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb	5.7 KB
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05. Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb	1.1 KB
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01. Defining-a-Function-in-Python-Lecture-Py3.ipynb	0.9 KB
/27. Python - Python Functions/
07. Built-in Functions in Python.mp4	10.7 MB
02. How to Create a Function with a Parameter.mp4	10.5 MB
03. Defining a Function in Python - Part II.mp4	6.8 MB
05. Conditional Statements and Functions.mp4	6.3 MB
04. How to Use a Function within a Function.mp4	3.4 MB
01. Defining a Function in Python.mp4	3.4 MB
06. Functions Containing a Few Arguments.mp4	2.9 MB
02. How to Create a Function with a Parameter.vtt	4.6 KB
07. Built-in Functions in Python.vtt	4.4 KB
05. Conditional Statements and Functions.vtt	3.7 KB
03. Defining a Function in Python - Part II.vtt	3.1 KB
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04. How to Use a Function within a Function.vtt	2.1 KB
06. Functions Containing a Few Arguments.vtt	1.5 KB
/assets/
05. Dictionaries-Solution-Py3.ipynb	6.3 KB
03. List-Slicing-Lecture-Py3.ipynb	5.1 KB
04. Tuples-Solution-Py3.ipynb	4.7 KB
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04. Tuples-Exercise-Py3.ipynb	2.1 KB
02. Help-Yourself-with-Methods-Exercise-Py3.ipynb	2.0 KB
/28. Python - Sequences/
05. Dictionaries.mp4	34.0 MB
02. Using Methods.mp4	31.8 MB
01. Lists.mp4	24.2 MB
03. List Slicing.mp4	20.1 MB
04. Tuples.mp4	19.1 MB
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05. Dictionaries.vtt	9.1 KB
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03. List Slicing.vtt	5.7 KB
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02. While-Loops-and-Incrementing-Lecture-Py3.ipynb	1.1 KB
06. Iterating-over-Dictionaries-Lecture-Py3.ipynb	1.1 KB
/29. Python - Iterations/
02. While Loops and Incrementing.mp4	21.2 MB
06. How to Iterate over Dictionaries.mp4	19.3 MB
04. Conditional Statements and Loops.mp4	18.2 MB
03. Lists with the range() Function.mp4	16.8 MB
01. For Loops.mp4	13.6 MB
05. Conditional Statements, Functions, and Loops.mp4	4.5 MB
03. Lists with the range() Function.vtt	8.8 KB
04. Conditional Statements and Loops.vtt	8.2 KB
06. How to Iterate over Dictionaries.vtt	8.0 KB
01. For Loops.vtt	6.9 KB
02. While Loops and Incrementing.vtt	6.2 KB
05. Conditional Statements, Functions, and Loops.vtt	2.5 KB
/30. Python - Advanced Python Tools/
04. Importing Modules in Python.mp4	10.4 MB
01. Object Oriented Programming.mp4	9.1 MB
03. What is the Standard Library.mp4	5.3 MB
02. Modules and Packages.mp4	2.2 MB
01. Object Oriented Programming.vtt	7.0 KB
04. Importing Modules in Python.vtt	5.0 KB
03. What is the Standard Library.vtt	4.0 KB
02. Modules and Packages.vtt	1.5 KB
/assets/
01. Course-notes-regression-analysis.pdf	319.7 KB
/external-links/
01. Advanced-Statistics-Flashcards.url	0.0 KB
/31. Part 5 Advanced Statistical Methods in Python/
01. Introduction to Regression Analysis.mp4	3.8 MB
01. Introduction to Regression Analysis.vtt	2.4 KB
/assets/
01. Course-notes-regression-analysis.pdf	319.7 KB
05. Simple-linear-regression-with-comments.ipynb	4.2 KB
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05. 1.01.Simple-linear-regression.csv	0.9 KB
/32. Advanced Statistical Methods - Linear Regression with StatsModels/
05. First Regression in Python.mp4	31.0 MB
08. How to Interpret the Regression Table.mp4	30.1 MB
04. Python Packages Installation.mp4	24.8 MB
10. What is the OLS.mp4	23.6 MB
01. The Linear Regression Model.mp4	14.1 MB
11. R-Squared.mp4	11.7 MB
09. Decomposition of Variability.mp4	9.2 MB
07. Using Seaborn for Graphs.mp4	7.7 MB
02. Correlation vs Regression.mp4	4.0 MB
03. Geometrical Representation of the Linear Regression Model.mp4	2.4 MB
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11. R-Squared.vtt	7.0 KB
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10. What is the OLS.vtt	3.9 KB
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03. Geometrical Representation of the Linear Regression Model.vtt	1.8 KB
07. Using Seaborn for Graphs.vtt	1.6 KB
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12. Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb	18.4 KB
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/33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/
02. Adjusted R-Squared.mp4	35.9 MB
08. A3 Normality and Homoscedasticity.mp4	28.7 MB
11. Dealing with Categorical Data - Dummy Variables.mp4	23.7 MB
13. Making Predictions with the Linear Regression.mp4	17.1 MB
07. A2 No Endogeneity.mp4	9.7 MB
09. A4 No Autocorrelation.mp4	8.3 MB
10. A5 No Multicollinearity.mp4	8.0 MB
04. Test for Significance of the Model (F-Test).mp4	7.5 MB
01. Multiple Linear Regression.mp4	6.0 MB
05. OLS Assumptions.mp4	5.5 MB
06. A1 Linearity.mp4	3.7 MB
11. Dealing with Categorical Data - Dummy Variables.vtt	9.8 KB
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08. A3 Normality and Homoscedasticity.vtt	7.2 KB
07. A2 No Endogeneity.vtt	5.7 KB
09. A4 No Autocorrelation.vtt	5.1 KB
10. A5 No Multicollinearity.vtt	4.7 KB
13. Making Predictions with the Linear Regression.vtt	4.5 KB
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05. OLS Assumptions.vtt	3.1 KB
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12. Dealing with Categorical Data - Dummy Variables.html	0.1 KB
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16. sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb	30.5 KB
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14. SKLEAR-1.IPY	13.2 KB
11. sklearn-How-to-properly-include-p-values.ipynb	13.0 KB
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19. sklearn-Train-Test-Split-with-comments.ipynb	9.3 KB
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17. sklearn-Feature-Scaling-Exercise.ipynb	6.2 KB
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13. real-estate-price-size-year.csv	2.4 KB
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16. 1.02.Multiple-linear-regression.csv	1.1 KB
15. 1.02.Multiple-linear-regression.csv	1.1 KB
08. 1.02.Multiple-linear-regression.csv	1.1 KB
12. 1.02.Multiple-linear-regression.csv	1.1 KB
10. 1.02.Multiple-linear-regression.csv	1.1 KB
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04. 1.01.Simple-linear-regression.csv	0.9 KB
03. 1.01.Simple-linear-regression.csv	0.9 KB
/34. Advanced Statistical Methods - Linear Regression with sklearn/
19. Train - Test Split Explained.mp4	37.3 MB
03. Simple Linear Regression with sklearn.mp4	28.8 MB
15. Feature Selection through Standardization of Weights.mp4	25.7 MB
04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4	23.4 MB
10. Feature Selection (F-regression).mp4	21.5 MB
16. Predicting with the Standardized Coefficients.mp4	21.4 MB
14. Feature Scaling (Standardization).mp4	21.4 MB
08. Calculating the Adjusted R-Squared in sklearn.mp4	17.7 MB
01. What is sklearn and How is it Different from Other Packages.mp4	8.9 MB
07. Multiple Linear Regression with sklearn.mp4	8.7 MB
12. Creating a Summary Table with P-values.mp4	6.8 MB
18. Underfitting and Overfitting.mp4	6.1 MB
02. How are we Going to Approach this Section.mp4	5.6 MB
19. Train - Test Split Explained.vtt	9.9 KB
14. Feature Scaling (Standardization).vtt	9.0 KB
15. Feature Selection through Standardization of Weights.vtt	7.8 KB
03. Simple Linear Regression with sklearn.vtt	7.8 KB
10. Feature Selection (F-regression).vtt	7.1 KB
04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt	6.9 KB
08. Calculating the Adjusted R-Squared in sklearn.vtt	6.8 KB
16. Predicting with the Standardized Coefficients.vtt	5.7 KB
07. Multiple Linear Regression with sklearn.vtt	4.4 KB
18. Underfitting and Overfitting.vtt	3.8 KB
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12. Creating a Summary Table with P-values.vtt	3.1 KB
02. How are we Going to Approach this Section.vtt	3.1 KB
05. A Note on Normalization.html	0.7 KB
11. A Note on Calculation of P-values with sklearn.html	0.4 KB
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13. Multiple Linear Regression - Exercise.html	0.1 KB
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04. sklearn-Linear-Regression-Practical-Example-Part-3-.url	0.1 KB
/35. Advanced Statistical Methods - Practical Example Linear Regression/
01. Practical Example Linear Regression (Part 1).mp4	88.9 MB
08. Practical Example Linear Regression (Part 5).mp4	52.9 MB
06. Practical Example Linear Regression (Part 4).mp4	41.3 MB
02. Practical Example Linear Regression (Part 2).mp4	33.4 MB
04. Practical Example Linear Regression (Part 3).mp4	17.5 MB
01. Practical Example Linear Regression (Part 1).vtt	15.2 KB
06. Practical Example Linear Regression (Part 4).vtt	12.1 KB
08. Practical Example Linear Regression (Part 5).vtt	11.3 KB
02. Practical Example Linear Regression (Part 2).vtt	8.5 KB
04. Practical Example Linear Regression (Part 3).vtt	4.6 KB
03. A Note on Multicollinearity.html	0.8 KB
07. Dummy Variables - Exercise.html	0.7 KB
09. Linear Regression - Exercise.html	0.5 KB
05. Dummies and Variance Inflation Factor - Exercise.html	0.1 KB
/assets/
01. Course-Notes-Logistic-Regression.pdf	343.2 KB
02. Course-Notes-Logistic-Regression.pdf	343.2 KB
16. Testing-the-Model-Solution.ipynb	113.8 KB
13. Calculating-the-Accuracy-of-the-Model-Solution.ipynb	83.2 KB
11. Bank-data.csv	20.0 KB
13. Bank-data.csv	20.0 KB
16. Bank-data.csv	20.0 KB
08. Bank-data.csv	20.0 KB
12. Accuracy-with-comments.ipynb	12.0 KB
16. Bank-data-testing.csv	8.5 KB
15. Testing-the-model-with-comments.ipynb	7.7 KB
16. Testing-the-Model-Exercise.ipynb	7.0 KB
05. Example-bank-data.csv	6.4 KB
15. Testing-the-model.ipynb	5.9 KB
13. Calculating-the-Accuracy-of-the-Model-Exercise.ipynb	5.5 KB
02. Admittance-with-comments.ipynb	5.4 KB
08. Understanding-Logistic-Regression-Tables-Solution.ipynb	4.9 KB
11. Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb	4.6 KB
05. Building-a-Logistic-Regression-Solution.ipynb	4.6 KB
04. Admittance-regression-tables-fixed-error.ipynb	4.2 KB
12. Accuracy.ipynb	3.7 KB
02. Admittance.ipynb	3.6 KB
08. Understanding-Logistic-Regression-Tables-Exercise.ipynb	3.2 KB
05. Building-a-Logistic-Regression-Exercise.ipynb	3.0 KB
10. 2.02.Binary-predictors.csv	2.6 KB
11. Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb	2.6 KB
04. Admittance-regression-summary-error.ipynb	2.5 KB
10. Binary-predictors.ipynb	2.5 KB
04. Admittance-regression.ipynb	2.1 KB
02. 2.01.Admittance.csv	1.6 KB
15. 2.03.Test-dataset.csv	0.3 KB
/36. Advanced Statistical Methods - Logistic Regression/
10. Binary Predictors in a Logistic Regression.mp4	26.1 MB
03. Logistic vs Logit Function.mp4	24.9 MB
02. A Simple Example in Python.mp4	22.9 MB
15. Testing the Model.mp4	22.6 MB
12. Calculating the Accuracy of the Model.mp4	21.2 MB
06. An Invaluable Coding Tip.mp4	19.7 MB
07. Understanding Logistic Regression Tables.mp4	15.3 MB
09. What do the Odds Actually Mean.mp4	11.9 MB
04. Building a Logistic Regression.mp4	9.0 MB
14. Underfitting and Overfitting.mp4	7.8 MB
01. Introduction to Logistic Regression.mp4	6.2 MB
15. Testing the Model.vtt	6.7 KB
02. A Simple Example in Python.vtt	6.0 KB
07. Understanding Logistic Regression Tables.vtt	5.7 KB
10. Binary Predictors in a Logistic Regression.vtt	5.4 KB
14. Underfitting and Overfitting.vtt	5.3 KB
03. Logistic vs Logit Function.vtt	5.2 KB
09. What do the Odds Actually Mean.vtt	4.5 KB
12. Calculating the Accuracy of the Model.vtt	4.4 KB
04. Building a Logistic Regression.vtt	3.6 KB
06. An Invaluable Coding Tip.vtt	3.2 KB
01. Introduction to Logistic Regression.vtt	1.9 KB
05. Building a Logistic Regression - Exercise.html	0.1 KB
11. Binary Predictors in a Logistic Regression - Exercise.html	0.1 KB
13. Calculating the Accuracy of the Model.html	0.1 KB
08. Understanding Logistic Regression Tables - Exercise.html	0.1 KB
16. Testing the Model - Exercise.html	0.1 KB
/assets/
02. Course-Notes-Cluster-Analysis.pdf	213.7 KB
01. Course-Notes-Cluster-Analysis.pdf	213.7 KB
/37. Advanced Statistical Methods - Cluster Analysis/
02. Some Examples of Clusters.mp4	37.6 MB
01. Introduction to Cluster Analysis.mp4	15.2 MB
03. Difference between Classification and Clustering.mp4	10.1 MB
04. Math Prerequisites.mp4	5.5 MB
02. Some Examples of Clusters.vtt	6.4 KB
01. Introduction to Cluster Analysis.vtt	5.1 KB
04. Math Prerequisites.vtt	4.5 KB
03. Difference between Classification and Clustering.vtt	3.7 KB
/assets/
15. Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb	15.7 KB
15. Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb	11.0 KB
05. Categorical.csv	10.6 KB
07. How-to-Choose-the-Number-of-Clusters-Solution.ipynb	8.7 KB
03. Countries-exercise.csv	8.5 KB
07. Countries-exercise.csv	8.5 KB
06. Selecting-the-number-of-clusters-with-comments.ipynb	7.7 KB
14. Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb	7.5 KB
12. Market-segmentation-example-Part2-with-comments.ipynb	7.0 KB
11. Market-segmentation-example-with-comments.ipynb	6.0 KB
02. Country-clusters-with-comments.ipynb	5.9 KB
04. Categorical-data-with-comments.ipynb	5.8 KB
07. How-to-Choose-the-Number-of-Clusters-Exercise.ipynb	5.7 KB
05. Clustering-Categorical-Data-Solution.ipynb	5.0 KB
12. Market-segmentation-example-Part2.ipynb	4.8 KB
03. A-Simple-Example-of-Clustering-Solution.ipynb	4.8 KB
06. Selecting-the-number-of-clusters.ipynb	4.6 KB
14. Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb	4.6 KB
11. Market-segmentation-example.ipynb	3.9 KB
05. Clustering-Categorical-Data-Exercise.ipynb	3.9 KB
15. iris-with-answers.csv	3.7 KB
03. A-Simple-Example-of-Clustering-Exercise.ipynb	3.7 KB
04. Categorical-data.ipynb	3.4 KB
02. Country-clusters.ipynb	3.4 KB
15. iris-dataset.csv	2.5 KB
14. iris-dataset.csv	2.5 KB
11. 3.12.Example.csv	0.3 KB
02. 3.01.Country-clusters.csv	0.2 KB
/38. Advanced Statistical Methods - K-Means Clustering/
13. How is Clustering Useful.mp4	39.3 MB
02. A Simple Example of Clustering.mp4	35.8 MB
12. Market Segmentation with Cluster Analysis (Part 2).mp4	35.7 MB
11. Market Segmentation with Cluster Analysis (Part 1).mp4	29.4 MB
06. How to Choose the Number of Clusters.mp4	28.2 MB
08. Pros and Cons of K-Means Clustering.mp4	11.7 MB
09. To Standardize or not to Standardize.mp4	11.4 MB
01. K-Means Clustering.mp4	11.3 MB
04. Clustering Categorical Data.mp4	10.9 MB
10. Relationship between Clustering and Regression.mp4	3.7 MB
02. A Simple Example of Clustering.vtt	10.0 KB
12. Market Segmentation with Cluster Analysis (Part 2).vtt	9.4 KB
06. How to Choose the Number of Clusters.vtt	7.7 KB
11. Market Segmentation with Cluster Analysis (Part 1).vtt	7.7 KB
13. How is Clustering Useful.vtt	6.9 KB
01. K-Means Clustering.vtt	6.8 KB
09. To Standardize or not to Standardize.vtt	6.4 KB
08. Pros and Cons of K-Means Clustering.vtt	4.7 KB
04. Clustering Categorical Data.vtt	3.4 KB
10. Relationship between Clustering and Regression.vtt	2.3 KB
05. Clustering Categorical Data - Exercise.html	0.1 KB
03. A Simple Example of Clustering - Exercise.html	0.1 KB
14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html	0.1 KB
15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html	0.1 KB
07. How to Choose the Number of Clusters - Exercise.html	0.1 KB
/assets/
03. Heatmaps-with-comments.ipynb	18.1 KB
03. Heatmaps.ipynb	1.9 KB
03. Country-clusters-standardized.csv	0.2 KB
/39. Advanced Statistical Methods - Other Types of Clustering/
03. Heatmaps.mp4	19.4 MB
02. Dendrogram.mp4	19.2 MB
01. Types of Clustering.mp4	9.4 MB
02. Dendrogram.vtt	7.8 KB
03. Heatmaps.vtt	6.3 KB
01. Types of Clustering.vtt	5.2 KB
/assets/
16. movies-metadata.zip	12.6 MB
13. Marvel-Comics.zip	3.7 MB
16. ratings-small.csv	2.4 MB
10. Properties-analysis.ipynb	293.4 KB
19. interactions.csv	75.0 KB
08. Furniture-store-data-analysis.ipynb	53.6 KB
06. orders.csv	38.6 KB
19. posts.csv	31.5 KB
14. Marvel-Comics-Reg-Ex.ipynb	30.2 KB
17. Movies-Data-Base-Recommendation-Engine.ipynb	20.9 KB
04. Data-Preprocessing-Medical-Data.ipynb	7.7 KB
19. friendships.csv	6.1 KB
12. Students-Hypothesis-Testing.ipynb	5.7 KB
05. Medical-Data-ML-Attempt.ipynb	4.5 KB
19. users.csv	3.6 KB
06. ratings.csv	3.5 KB
05. patients-preprocessed.csv	3.4 KB
04. patients.csv	3.0 KB
10. properties.csv	2.7 KB
12. students.csv	2.1 KB
06. products.csv	1.8 KB
06. customers.csv	1.6 KB
05. diagnosis-mapping.csv	0.1 KB
/40. ChatGPT for Data Science/
05. First attempt at machine learning with ChatGPT.mp4	38.5 MB
10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4	35.3 MB
19. Using ChatGPT for ethical considerations.mp4	35.2 MB
14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4	34.7 MB
04. Data Preprocessing with ChatGPT.mp4	30.1 MB
08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp4	28.5 MB
01. Traditional data science methods and the role of ChatGPT.mp4	27.4 MB
06. Analyzing a client database with ChatGPT in Python.mp4	22.7 MB
09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4	22.6 MB
17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp4	18.7 MB
16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4	18.1 MB
07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp4	15.9 MB
13. Marvels comic book database Intro to Regular Expressions (RegEx).mp4	15.7 MB
18. Ethical principles in data and AI utilization.mp4	15.4 MB
12. Hypothesis testing with ChatGPT.mp4	15.1 MB
03. How ChatGPT can boost your productivity.mp4	5.6 MB
02. How to install ChatGPT.mp4	5.5 MB
10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.vtt	7.7 KB
19. Using ChatGPT for ethical considerations.vtt	7.7 KB
09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.vtt	7.6 KB
01. Traditional data science methods and the role of ChatGPT.vtt	7.4 KB
14. Decoding comic book data Python Regular Expressions and ChatGPT.vtt	6.6 KB
04. Data Preprocessing with ChatGPT.vtt	6.6 KB
05. First attempt at machine learning with ChatGPT.vtt	6.5 KB
17. Algorithm recommendation recommendation engine for movies with ChatGPT.vtt	6.5 KB
08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.vtt	6.0 KB
12. Hypothesis testing with ChatGPT.vtt	5.8 KB
06. Analyzing a client database with ChatGPT in Python.vtt	5.3 KB
07. Analyzing a client database with ChatGPT in Python – analyzing top products.vtt	5.3 KB
16. Algorithm recommendation Movie Database Analysis with ChatGPT.vtt	4.5 KB
18. Ethical principles in data and AI utilization.vtt	4.5 KB
13. Marvels comic book database Intro to Regular Expressions (RegEx).vtt	2.8 KB
03. How ChatGPT can boost your productivity.vtt	2.5 KB
02. How to install ChatGPT.vtt	2.1 KB
11. Assignment 1.html	1.7 KB
15. Assignment 2.html	1.6 KB
/assets/
12. 365-User-Reviews-Naive-Bayes-Sentiment-Analysis.ipynb	1.8 MB
12. user-courses-review-test-set.csv	20.1 KB
/41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/
02. The Naive Bayes Algorithm.mp4	44.1 MB
10. Machine Learning with Naïve Bayes (First Attempt).mp4	29.5 MB
12. Testing the Model on New Data.mp4	21.8 MB
11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp4	19.8 MB
07. Optimizing User Reviews Data Preprocessing & EDA.mp4	19.6 MB
08. Reg Ex for Analyzing Text Review Data.mp4	17.0 MB
03. Tokenization and Vectorization.mp4	16.6 MB
06. Loading the Dataset and Preprocessing.mp4	15.5 MB
05. Overcome Imbalanced Data in Machine Learning.mp4	15.3 MB
09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp4	14.5 MB
01. Intro to the Case Study.mp4	10.9 MB
04. Imbalanced Data Sets.mp4	6.9 MB
10. Machine Learning with Naïve Bayes (First Attempt).vtt	8.6 KB
03. Tokenization and Vectorization.vtt	8.1 KB
12. Testing the Model on New Data.vtt	7.1 KB
11. Machine Learning with Naïve Bayes – converting the problem to a binary one.vtt	6.9 KB
02. The Naive Bayes Algorithm.vtt	6.2 KB
07. Optimizing User Reviews Data Preprocessing & EDA.vtt	6.1 KB
09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.vtt	5.5 KB
08. Reg Ex for Analyzing Text Review Data.vtt	5.2 KB
05. Overcome Imbalanced Data in Machine Learning.vtt	5.2 KB
01. Intro to the Case Study.vtt	3.8 KB
06. Loading the Dataset and Preprocessing.vtt	3.8 KB
04. Imbalanced Data Sets.vtt	3.3 KB
/assets/
04. Scalars-Vectors-and-Matrices.ipynb	4.7 KB
10. Dot-product-Part-2.ipynb	3.7 KB
06. Adding-and-subtracting-matrices.ipynb	3.3 KB
07. Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb	3.2 KB
08. Tranpose-of-a-matrix.ipynb	3.0 KB
09. Dot-product.ipynb	2.2 KB
05. Tensors.ipynb	2.1 KB
/external-links/
01. Math-Flashcards.url	0.1 KB
/42. Part 6 Mathematics/
11. Why is Linear Algebra Useful.mp4	92.8 MB
10. Dot Product of Matrices.mp4	36.0 MB
06. Addition and Subtraction of Matrices.mp4	23.2 MB
04. Arrays in Python - A Convenient Way To Represent Matrices.mp4	19.9 MB
05. What is a Tensor.mp4	16.3 MB
08. Transpose of a Matrix.mp4	14.9 MB
03. Linear Algebra and Geometry.mp4	14.4 MB
09. Dot Product.mp4	13.5 MB
01. What is a Matrix.mp4	12.5 MB
02. Scalars and Vectors.mp4	9.0 MB
07. Errors when Adding Matrices.mp4	6.1 MB
11. Why is Linear Algebra Useful.vtt	11.8 KB
10. Dot Product of Matrices.vtt	9.3 KB
04. Arrays in Python - A Convenient Way To Represent Matrices.vtt	6.4 KB
08. Transpose of a Matrix.vtt	5.7 KB
01. What is a Matrix.vtt	4.7 KB
09. Dot Product.vtt	4.4 KB
06. Addition and Subtraction of Matrices.vtt	4.3 KB
03. Linear Algebra and Geometry.vtt	4.2 KB
02. Scalars and Vectors.vtt	4.1 KB
05. What is a Tensor.vtt	3.9 KB
07. Errors when Adding Matrices.vtt	2.8 KB
/43. Part 7 Deep Learning/
01. What to Expect from this Part.mp4	12.3 MB
01. What to Expect from this Part.vtt	4.9 KB
/assets/
02. Course-Notes-Section-2.pdf	592.0 KB
01. Course-Notes-Section-2.pdf	592.0 KB
11. GD-function-example.xlsx	43.4 KB
/44. Deep Learning - Introduction to Neural Networks/
11. Optimization Algorithm 1-Parameter Gradient Descent.mp4	24.7 MB
12. Optimization Algorithm n-Parameter Gradient Descent.mp4	17.7 MB
06. The Linear model with Multiple Inputs and Multiple Outputs.mp4	17.4 MB
03. Types of Machine Learning.mp4	13.7 MB
01. Introduction to Neural Networks.mp4	11.0 MB
10. Common Objective Functions Cross-Entropy Loss.mp4	10.3 MB
04. The Linear Model (Linear Algebraic Version).mp4	8.4 MB
05. The Linear Model with Multiple Inputs.mp4	8.3 MB
07. Graphical Representation of Simple Neural Networks.mp4	8.2 MB
02. Training the Model.mp4	8.1 MB
08. What is the Objective Function.mp4	6.5 MB
09. Common Objective Functions L2-norm Loss.mp4	5.7 MB
11. Optimization Algorithm 1-Parameter Gradient Descent.vtt	9.0 KB
12. Optimization Algorithm n-Parameter Gradient Descent.vtt	8.0 KB
01. Introduction to Neural Networks.vtt	6.4 KB
03. Types of Machine Learning.vtt	5.6 KB
10. Common Objective Functions Cross-Entropy Loss.vtt	5.5 KB
06. The Linear model with Multiple Inputs and Multiple Outputs.vtt	5.0 KB
02. Training the Model.vtt	4.9 KB
04. The Linear Model (Linear Algebraic Version).vtt	3.8 KB
09. Common Objective Functions L2-norm Loss.vtt	3.0 KB
05. The Linear Model with Multiple Inputs.vtt	2.9 KB
07. Graphical Representation of Simple Neural Networks.vtt	2.8 KB
08. What is the Objective Function.vtt	2.3 KB
/assets/
01. Shortcuts-for-Jupyter.pdf	634.0 KB
05. Minimal-example-Exercise-3.d.Solution.ipynb	86.2 KB
05. Minimal-example-Exercise-3.c.Solution.ipynb	71.8 KB
05. Minimal-example-Exercise-1-Solution.ipynb	70.7 KB
05. Minimal-example-Exercise-5-Solution.ipynb	70.5 KB
05. Minimal-example-Exercise-3.a.Solution.ipynb	69.5 KB
05. Minimal-example-Exercise-3.b.Solution.ipynb	69.3 KB
05. Minimal-example-Exercise-4-Solution.ipynb	68.1 KB
05. Minimal-example-Exercise-6-Solution.ipynb	63.2 KB
05. Minimal-example-Exercise-6.ipynb	63.2 KB
05. Minimal-example-Exercise-2-Solution.ipynb	62.9 KB
05. Minimal-example-All-Exercises.ipynb	13.2 KB
04. Minimal-example-Part-4-Complete.ipynb	11.7 KB
03. Minimal-example-Part-3.ipynb	7.0 KB
02. Minimal-example-Part-2.ipynb	3.7 KB
01. Minimal-example-Part-1.ipynb	1.2 KB
/45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/
04. Basic NN Example (Part 4).mp4	41.9 MB
03. Basic NN Example (Part 3).mp4	16.4 MB
02. Basic NN Example (Part 2).mp4	16.0 MB
01. Basic NN Example (Part 1).mp4	9.8 MB
04. Basic NN Example (Part 4).vtt	11.2 KB
02. Basic NN Example (Part 2).vtt	6.8 KB
01. Basic NN Example (Part 1).vtt	4.6 KB
03. Basic NN Example (Part 3).vtt	4.5 KB
05. Basic NN Example Exercises.html	1.7 KB
/assets/
01. Shortcuts-for-Jupyter.pdf	634.0 KB
09. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb	86.5 KB
09. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb	85.7 KB
09. TensorFlow-Minimal-example-All-exercises.ipynb	85.6 KB
08. TensorFlow-Minimal-example-complete-with-comments.ipynb	84.3 KB
09. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb	79.4 KB
08. TensorFlow-Minimal-example-complete.ipynb	78.7 KB
07. TensorFlow-Minimal-example-Part3.ipynb	78.4 KB
09. TensorFlow-Minimal-example-Exercise-1-Solution.ipynb	28.6 KB
06. TensorFlow-Minimal-example-Part2.ipynb	9.3 KB
05. TensorFlow-Minimal-example-Part1.ipynb	1.7 KB
/46. Deep Learning - TensorFlow 2.0 Introduction/
01. How to Install TensorFlow 2.0.mp4	28.7 MB
06. Outlining the Model with TensorFlow 2.mp4	28.3 MB
07. Interpreting the Result and Extracting the Weights and Bias.mp4	27.2 MB
08. Customizing a TensorFlow 2 Model.mp4	17.6 MB
03. TensorFlow 1 vs TensorFlow 2.mp4	16.0 MB
02. TensorFlow Outline and Comparison with Other Libraries.mp4	16.0 MB
05. Types of File Formats Supporting TensorFlow.mp4	9.3 MB
04. A Note on TensorFlow 2 Syntax.mp4	4.9 MB
06. Outlining the Model with TensorFlow 2.vtt	8.6 KB
07. Interpreting the Result and Extracting the Weights and Bias.vtt	6.9 KB
01. How to Install TensorFlow 2.0.vtt	6.7 KB
02. TensorFlow Outline and Comparison with Other Libraries.vtt	5.6 KB
08. Customizing a TensorFlow 2 Model.vtt	4.4 KB
03. TensorFlow 1 vs TensorFlow 2.vtt	4.1 KB
05. Types of File Formats Supporting TensorFlow.vtt	3.5 KB
04. A Note on TensorFlow 2 Syntax.vtt	1.5 KB
09. Basic NN with TensorFlow Exercises.html	1.3 KB
/assets/
02. Course-Notes-Section-6.pdf	958.9 KB
01. Course-Notes-Section-6.pdf	958.9 KB
09. Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf	186.8 KB
/47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/
03. Digging into a Deep Net.mp4	24.8 MB
04. Non-Linearities and their Purpose.mp4	23.6 MB
07. Backpropagation.mp4	21.3 MB
02. What is a Deep Net.mp4	9.6 MB
05. Activation Functions.mp4	9.3 MB
06. Activation Functions Softmax Activation.mp4	9.2 MB
08. Backpropagation Picture.mp4	8.5 MB
01. What is a Layer.mp4	5.4 MB
03. Digging into a Deep Net.vtt	7.1 KB
05. Activation Functions.vtt	5.4 KB
07. Backpropagation.vtt	4.7 KB
06. Activation Functions Softmax Activation.vtt	4.6 KB
04. Non-Linearities and their Purpose.vtt	4.3 KB
08. Backpropagation Picture.vtt	3.9 KB
02. What is a Deep Net.vtt	3.3 KB
01. What is a Layer.vtt	2.7 KB
09. Backpropagation - A Peek into the Mathematics of Optimization.html	0.5 KB
/48. Deep Learning - Overfitting/
02. Underfitting and Overfitting for Classification.mp4	14.7 MB
01. What is Overfitting.mp4	11.3 MB
06. Early Stopping or When to Stop Training.mp4	10.8 MB
04. Training, Validation, and Test Datasets.mp4	9.9 MB
03. What is Validation.mp4	8.8 MB
05. N-Fold Cross Validation.mp4	6.5 MB
06. Early Stopping or When to Stop Training.vtt	7.1 KB
01. What is Overfitting.vtt	6.0 KB
03. What is Validation.vtt	5.1 KB
05. N-Fold Cross Validation.vtt	4.5 KB
04. Training, Validation, and Test Datasets.vtt	3.5 KB
02. Underfitting and Overfitting for Classification.vtt	2.9 KB
/49. Deep Learning - Initialization/
01. What is Initialization.mp4	9.3 MB
02. Types of Simple Initializations.mp4	6.0 MB
03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4	5.7 MB
03. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt	3.9 KB
02. Types of Simple Initializations.vtt	3.9 KB
01. What is Initialization.vtt	3.8 KB
/50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/
04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4	18.4 MB
06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4	8.9 MB
01. Stochastic Gradient Descent.mp4	8.2 MB
07. Adam (Adaptive Moment Estimation).mp4	7.5 MB
03. Momentum.mp4	5.4 MB
02. Problems with Gradient Descent.mp4	3.8 MB
05. Learning Rate Schedules Visualized.mp4	3.3 MB
04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt	6.5 KB
06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt	5.7 KB
01. Stochastic Gradient Descent.vtt	4.9 KB
03. Momentum.vtt	3.7 KB
07. Adam (Adaptive Moment Estimation).vtt	3.5 KB
02. Problems with Gradient Descent.vtt	3.1 KB
05. Learning Rate Schedules Visualized.vtt	2.3 KB
/51. Deep Learning - Preprocessing/
03. Standardization.mp4	12.7 MB
01. Preprocessing Introduction.mp4	9.7 MB
05. Binary and One-Hot Encoding.mp4	9.0 MB
04. Preprocessing Categorical Data.mp4	5.7 MB
02. Types of Basic Preprocessing.mp4	3.4 MB
03. Standardization.vtt	6.3 KB
05. Binary and One-Hot Encoding.vtt	5.4 KB
01. Preprocessing Introduction.vtt	4.2 KB
04. Preprocessing Categorical Data.vtt	2.9 KB
02. Types of Basic Preprocessing.vtt	1.9 KB
/assets/
11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb	21.1 KB
11. TensorFlow-MNIST-All-Exercises.ipynb	17.1 KB
11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb	16.2 KB
11. 2.TensorFlow-MNIST-Depth-Solution.ipynb	15.7 KB
11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb	15.7 KB
11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb	15.5 KB
11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb	15.5 KB
11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb	15.5 KB
11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb	15.4 KB
11. 1.TensorFlow-MNIST-Width-Solution.ipynb	15.2 KB
11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb	15.1 KB
12. TensorFlow-MNIST-complete-with-comments.ipynb	14.9 KB
10. TensorFlow-MNIST-Part6-with-comments.ipynb	12.8 KB
09. TensorFlow-MNIST-Part5-with-comments.ipynb	11.2 KB
08. TensorFlow-MNIST-Part4-with-comments.ipynb	10.7 KB
07. TensorFlow-MNIST-Part3-with-comments.ipynb	8.8 KB
12. TensorFlow-MNIST-complete.ipynb	6.9 KB
05. TensorFlow-MNIST-Part2-with-comments.ipynb	6.5 KB
03. TensorFlow-MNIST-Part1-with-comments.ipynb	4.1 KB
/52. Deep Learning - Classifying on the MNIST Dataset/
06. MNIST Preprocess the Data - Shuffle and Batch.mp4	34.3 MB
10. MNIST Learning.mp4	32.5 MB
04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4	24.0 MB
12. MNIST Testing the Model.mp4	23.7 MB
08. MNIST Outline the Model.mp4	23.1 MB
03. MNIST Importing the Relevant Packages and Loading the Data.mp4	12.8 MB
09. MNIST Select the Loss and the Optimizer.mp4	11.2 MB
02. MNIST How to Tackle the MNIST.mp4	8.3 MB
01. MNIST The Dataset.mp4	4.8 MB
06. MNIST Preprocess the Data - Shuffle and Batch.vtt	9.8 KB
10. MNIST Learning.vtt	8.1 KB
08. MNIST Outline the Model.vtt	7.3 KB
04. MNIST Preprocess the Data - Create a Validation Set and Scale It.vtt	6.7 KB
12. MNIST Testing the Model.vtt	6.1 KB
01. MNIST The Dataset.vtt	3.7 KB
02. MNIST How to Tackle the MNIST.vtt	3.7 KB
03. MNIST Importing the Relevant Packages and Loading the Data.vtt	3.1 KB
09. MNIST Select the Loss and the Optimizer.vtt	3.1 KB
11. MNIST - Exercises.html	2.0 KB
07. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html	0.1 KB
05. MNIST Preprocess the Data - Scale the Test Data - Exercise.html	0.1 KB
/assets/
01. Audiobooks-data.csv	727.8 KB
08. TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb	20.2 KB
11. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb	12.2 KB
12. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb	12.2 KB
04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb	11.5 KB
09. TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb	10.3 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb	10.3 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb	8.8 KB
04. TensorFlow-Audiobooks-Preprocessing.ipynb	5.7 KB
07. TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb	4.7 KB
/53. Deep Learning - Business Case Example/
04. Business Case Preprocessing the Data.mp4	77.4 MB
01. Business Case Exploring the Dataset and Identifying Predictors.mp4	53.8 MB
09. Business Case Setting an Early Stopping Mechanism.mp4	45.9 MB
08. Business Case Learning and Interpreting the Result.mp4	30.8 MB
03. Business Case Balancing the Dataset.mp4	23.4 MB
06. Business Case Load the Preprocessed Data.mp4	14.5 MB
11. Business Case Testing the Model.mp4	8.6 MB
02. Business Case Outlining the Solution.mp4	3.2 MB
04. Business Case Preprocessing the Data.vtt	13.9 KB
01. Business Case Exploring the Dataset and Identifying Predictors.vtt	11.3 KB
09. Business Case Setting an Early Stopping Mechanism.vtt	8.3 KB
08. Business Case Learning and Interpreting the Result.vtt	6.5 KB
06. Business Case Load the Preprocessed Data.vtt	4.8 KB
03. Business Case Balancing the Dataset.vtt	4.4 KB
11. Business Case Testing the Model.vtt	2.2 KB
02. Business Case Outlining the Solution.vtt	2.0 KB
12. Business Case Final Exercise.html	0.4 KB
05. Business Case Preprocessing the Data - Exercise.html	0.4 KB
10. Setting an Early Stopping Mechanism - Exercise.html	0.2 KB
07. Business Case Load the Preprocessed Data - Exercise.html	0.1 KB
/54. Deep Learning - Conclusion/
06. An Overview of non-NN Approaches.mp4	16.9 MB
04. An overview of CNNs.mp4	14.0 MB
01. Summary on What You've Learned.mp4	10.3 MB
05. An Overview of RNNs.mp4	7.3 MB
02. What's Further out there in terms of Machine Learning.mp4	5.0 MB
04. An overview of CNNs.vtt	6.5 KB
06. An Overview of non-NN Approaches.vtt	5.9 KB
01. Summary on What You've Learned.vtt	5.6 KB
05. An Overview of RNNs.vtt	4.1 KB
02. What's Further out there in terms of Machine Learning.vtt	2.8 KB
03. DeepMind and Deep Learning.html	1.1 KB
/assets/
05. Shortcuts-for-Jupyter.pdf	634.0 KB
10. TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb	51.2 KB
10. TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb	27.6 KB
10. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb	27.4 KB
10. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb	26.2 KB
10. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb	26.1 KB
10. TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb	24.2 KB
10. TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb	22.3 KB
10. TensorFlow-Minimal-Example-All-Exercises.ipynb	14.3 KB
09. 5.6.TensorFlow-Minimal-example-complete.ipynb	12.4 KB
08. 5.5.TensorFlow-Minimal-example-Part-3.ipynb	8.9 KB
07. 5.4.TensorFlow-Minimal-example-Part-2.ipynb	6.3 KB
06. 5.3.TensorFlow-Minimal-example-Part-1.ipynb	3.4 KB
/55. Appendix Deep Learning - TensorFlow 1 Introduction/
07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4	18.5 MB
09. Basic NN Example with TF Model Output.mp4	17.9 MB
04. TensorFlow Intro.mp4	17.7 MB
08. Basic NN Example with TF Loss Function and Gradient Descent.mp4	14.3 MB
05. Actual Introduction to TensorFlow.mp4	9.5 MB
06. Types of File Formats, supporting Tensors.mp4	9.3 MB
02. How to Install TensorFlow 1.mp4	5.2 MB
07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt	8.2 KB
09. Basic NN Example with TF Model Output.vtt	8.0 KB
04. TensorFlow Intro.vtt	5.5 KB
08. Basic NN Example with TF Loss Function and Gradient Descent.vtt	5.0 KB
06. Types of File Formats, supporting Tensors.vtt	3.5 KB
02. How to Install TensorFlow 1.vtt	3.5 KB
05. Actual Introduction to TensorFlow.vtt	2.4 KB
03. A Note on Installing Packages in Anaconda.html	2.3 KB
10. Basic NN Example with TF Exercises.html	1.6 KB
01. READ ME!!!!.html	0.6 KB
/assets/
11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb	18.1 KB
11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb	17.2 KB
10. TensorFlow-MNIST-Exercises-All.ipynb	15.8 KB
11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb	15.6 KB
11. 2.TensorFlow-MNIST-Depth-Solution.ipynb	15.2 KB
11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb	14.7 KB
11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb	14.6 KB
11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb	14.5 KB
11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb	14.4 KB
11. 1.TensorFlow-MNIST-Width-Solution.ipynb	14.4 KB
11. 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb	14.3 KB
11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb	14.3 KB
09. 12.9.TensorFlow-MNIST-with-comments.ipynb	13.3 KB
08. 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb	11.8 KB
07. 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb	8.7 KB
06. 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb	8.1 KB
05. 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb	7.5 KB
04. 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb	6.2 KB
03. 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb	4.0 KB
/56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/
09. MNIST Results and Testing.mp4	40.0 MB
04. MNIST Model Outline.mp4	36.4 MB
08. MNIST Learning.mp4	33.4 MB
06. Calculating the Accuracy of the Model.mp4	25.6 MB
05. MNIST Loss and Optimization Algorithm.mp4	16.6 MB
03. MNIST Relevant Packages.mp4	11.8 MB
07. MNIST Batching and Early Stopping.mp4	9.9 MB
02. MNIST How to Tackle the MNIST.mp4	8.4 MB
01. MNIST What is the MNIST Dataset.mp4	5.0 MB
08. MNIST Learning.vtt	10.7 KB
04. MNIST Model Outline.vtt	9.4 KB
09. MNIST Results and Testing.vtt	8.4 KB
06. Calculating the Accuracy of the Model.vtt	5.4 KB
02. MNIST How to Tackle the MNIST.vtt	3.9 KB
01. MNIST What is the MNIST Dataset.vtt	3.7 KB
05. MNIST Loss and Optimization Algorithm.vtt	3.7 KB
07. MNIST Batching and Early Stopping.vtt	2.9 KB
11. MNIST Solutions.html	2.3 KB
03. MNIST Relevant Packages.vtt	2.2 KB
10. MNIST Exercises.html	2.2 KB
/assets/
12. Audiobooks-data.csv	727.8 KB
03. Audiobooks-data.csv	727.8 KB
04. Audiobooks-data.csv	727.8 KB
01. Audiobooks-data.csv	727.8 KB
05. Audiobooks-data.csv	727.8 KB
11. Audiobooks-data.csv	727.8 KB
11. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb	14.7 KB
12. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb	14.7 KB
08. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb	13.0 KB
09. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb	13.0 KB
04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb	11.5 KB
12. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb	11.5 KB
11. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb	11.5 KB
08. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb	10.9 KB
09. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb	10.9 KB
07. TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb	10.6 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb	10.3 KB
07. TensorFlow-Audiobooks-Outlining-the-model.ipynb	9.6 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb	8.8 KB
04. TensorFlow-Audiobooks-Preprocessing.ipynb	5.7 KB
/57. Appendix Deep Learning - TensorFlow 1 Business Case/
04. Business Case Preprocessing.mp4	78.0 MB
01. Business Case Getting Acquainted with the Dataset.mp4	63.2 MB
06. Creating a Data Provider.mp4	59.0 MB
07. Business Case Model Outline.mp4	44.6 MB
03. The Importance of Working with a Balanced Dataset.mp4	28.6 MB
08. Business Case Optimization.mp4	28.2 MB
11. Business Case A Comment on the Homework.mp4	21.6 MB
09. Business Case Interpretation.mp4	19.5 MB
10. Business Case Testing the Model.mp4	4.6 MB
02. Business Case Outlining the Solution.mp4	4.4 MB
04. Business Case Preprocessing.vtt	13.9 KB
01. Business Case Getting Acquainted with the Dataset.vtt	11.2 KB
06. Creating a Data Provider.vtt	8.5 KB
07. Business Case Model Outline.vtt	7.3 KB
08. Business Case Optimization.vtt	7.1 KB
11. Business Case A Comment on the Homework.vtt	5.5 KB
03. The Importance of Working with a Balanced Dataset.vtt	4.5 KB
09. Business Case Interpretation.vtt	3.2 KB
10. Business Case Testing the Model.vtt	2.8 KB
02. Business Case Outlining the Solution.vtt	2.7 KB
12. Business Case Final Exercise.html	0.4 KB
05. Business Case Preprocessing Exercise.html	0.4 KB
/58. Software Integration/
02. What are Data Connectivity, APIs, and Endpoints.mp4	63.1 MB
03. Taking a Closer Look at APIs.mp4	25.7 MB
01. What are Data, Servers, Clients, Requests, and Responses.mp4	20.5 MB
04. Communication between Software Products through Text Files.mp4	18.4 MB
05. Software Integration - Explained.mp4	16.8 MB
03. Taking a Closer Look at APIs.vtt	11.2 KB
02. What are Data Connectivity, APIs, and Endpoints.vtt	9.4 KB
05. Software Integration - Explained.vtt	7.2 KB
01. What are Data, Servers, Clients, Requests, and Responses.vtt	6.4 KB
04. Communication between Software Products through Text Files.vtt	6.0 KB
/59. Case Study - What's Next in the Course/
03. Introducing the Data Set.mp4	25.4 MB
01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4	20.6 MB
02. The Business Task.mp4	11.8 MB
01. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt	5.7 KB
03. Introducing the Data Set.vtt	4.4 KB
02. The Business Task.vtt	4.2 KB
/assets/
29. Absenteeism-Exercise-Preprocessing-LECTURES.ipynb	8.0 MB
01. data-preprocessing-homework.pdf	137.7 KB
01. Absenteeism-data.csv	32.8 KB
01. df-preprocessed.csv	29.8 KB
32. Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb	8.7 KB
29. Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb	8.5 KB
29. Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb	7.5 KB
23. Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb	4.9 KB
32. Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb	4.2 KB
/60. Case Study - Preprocessing the 'Absenteeism_data'/
11. Obtaining Dummies from a Single Feature.mp4	73.1 MB
03. Checking the Content of the Data Set.mp4	56.6 MB
16. Classifying the Various Reasons for Absence.mp4	53.8 MB
07. Dropping a Column from a DataFrame in Python.mp4	43.2 MB
26. Analyzing the Dates from the Initial Data Set.mp4	42.1 MB
27. Extracting the Month Value from the Date Column.mp4	35.5 MB
10. Analyzing the Reasons for Absence.mp4	29.0 MB
17. Using .concat() in Python.mp4	28.7 MB
31. Working on Education, Children, and Pets.mp4	28.3 MB
02. Importing the Absenteeism Data in Python.mp4	20.5 MB
28. Extracting the Day of the Week from the Date Column.mp4	20.1 MB
04. Introduction to Terms with Multiple Meanings.mp4	18.9 MB
23. Creating Checkpoints while Coding in Jupyter.mp4	18.2 MB
30. Analyzing Several Straightforward Columns for this Exercise.mp4	15.0 MB
32. Final Remarks of this Section.mp4	14.2 MB
20. Reordering Columns in a Pandas DataFrame in Python.mp4	10.5 MB
06. Using a Statistical Approach towards the Solution to the Exercise.mp4	10.4 MB
15. More on Dummy Variables A Statistical Perspective.mp4	6.1 MB
11. Obtaining Dummies from a Single Feature.vtt	10.8 KB
16. Classifying the Various Reasons for Absence.vtt	10.8 KB
26. Analyzing the Dates from the Initial Data Set.vtt	9.1 KB
07. Dropping a Column from a DataFrame in Python.vtt	8.4 KB
27. Extracting the Month Value from the Date Column.vtt	8.2 KB
03. Checking the Content of the Data Set.vtt	7.3 KB
10. Analyzing the Reasons for Absence.vtt	6.2 KB
31. Working on Education, Children, and Pets.vtt	6.1 KB
17. Using .concat() in Python.vtt	5.3 KB
28. Extracting the Day of the Week from the Date Column.vtt	4.9 KB
30. Analyzing Several Straightforward Columns for this Exercise.vtt	4.7 KB
04. Introduction to Terms with Multiple Meanings.vtt	4.4 KB
02. Importing the Absenteeism Data in Python.vtt	4.1 KB
23. Creating Checkpoints while Coding in Jupyter.vtt	3.8 KB
06. Using a Statistical Approach towards the Solution to the Exercise.vtt	3.1 KB
05. What's Regression Analysis - a Quick Refresher.html	2.9 KB
32. Final Remarks of this Section.vtt	2.7 KB
01. What to Expect from the Following Sections.html	2.5 KB
14. Dropping a Dummy Variable from the Data Set.html	2.4 KB
20. Reordering Columns in a Pandas DataFrame in Python.vtt	1.9 KB
15. More on Dummy Variables A Statistical Perspective.vtt	1.7 KB
29. EXERCISE - Removing the Date Column.html	1.2 KB
33. A Note on Exporting Your Data as a .csv File.html	0.9 KB
08. EXERCISE - Dropping a Column from a DataFrame in Python.html	0.9 KB
22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html	0.5 KB
18. EXERCISE - Using .concat() in Python.html	0.2 KB
21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html	0.2 KB
19. SOLUTION - Using .concat() in Python.html	0.1 KB
24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html	0.1 KB
12. EXERCISE - Obtaining Dummies from a Single Feature.html	0.1 KB
25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html	0.1 KB
13. SOLUTION - Obtaining Dummies from a Single Feature.html	0.1 KB
09. SOLUTION - Dropping a Column from a DataFrame in Python.html	0.1 KB
/assets/
01. Absenteeism-preprocessed.csv	29.8 KB
/external-links/
11. Logistic-Regression-prior-to-Backward-Elimination.url	0.2 KB
09. Logistic-Regression-prior-to-Custom-Scaler.url	0.2 KB
15. Logistic-Regression-with-Comments.url	0.2 KB
15. Logistic-Regression.url	0.2 KB
/61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/
08. Interpreting the Coefficients for Our Problem.mp4	43.1 MB
05. Splitting the Data for Training and Testing.mp4	37.8 MB
02. Creating the Targets for the Logistic Regression.mp4	34.0 MB
11. Backward Elimination or How to Simplify Your Model.mp4	33.4 MB
12. Testing the Model We Created.mp4	33.1 MB
16. Preparing the Deployment of the Model through a Module.mp4	29.9 MB
07. Creating a Summary Table with the Coefficients and Intercept.mp4	28.3 MB
13. Saving the Model and Preparing it for Deployment.mp4	26.8 MB
09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4	17.7 MB
06. Fitting the Model and Assessing its Accuracy.mp4	16.0 MB
10. Interpreting the Coefficients of the Logistic Regression.mp4	15.9 MB
04. Standardizing the Data.mp4	15.9 MB
01. Exploring the Problem with a Machine Learning Mindset.mp4	13.6 MB
03. Selecting the Inputs for the Logistic Regression.mp4	9.1 MB
02. Creating the Targets for the Logistic Regression.vtt	8.9 KB
08. Interpreting the Coefficients for Our Problem.vtt	8.7 KB
05. Splitting the Data for Training and Testing.vtt	8.7 KB
10. Interpreting the Coefficients of the Logistic Regression.vtt	7.7 KB
06. Fitting the Model and Assessing its Accuracy.vtt	7.5 KB
12. Testing the Model We Created.vtt	6.7 KB
07. Creating a Summary Table with the Coefficients and Intercept.vtt	6.5 KB
16. Preparing the Deployment of the Model through a Module.vtt	6.0 KB
13. Saving the Model and Preparing it for Deployment.vtt	5.9 KB
11. Backward Elimination or How to Simplify Your Model.vtt	5.5 KB
09. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt	5.4 KB
01. Exploring the Problem with a Machine Learning Mindset.vtt	4.9 KB
04. Standardizing the Data.vtt	4.4 KB
03. Selecting the Inputs for the Logistic Regression.vtt	3.7 KB
14. ARTICLE - A Note on 'pickling'.html	2.2 KB
15. EXERCISE - Saving the Model (and Scaler).html	0.3 KB
/assets/
01. Absenteeism-Exercise-Integration.ipynb	63.8 KB
01. absenteeism-module.py	6.8 KB
01. Absenteeism-new-data.csv	1.9 KB
01. scaler	1.9 KB
01. model	1.0 KB
04. Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb	1.0 KB
/62. Case Study - Loading the 'absenteeism_module'/
03. Deploying the 'absenteeism_module' - Part II.mp4	47.3 MB
02. Deploying the 'absenteeism_module' - Part I.mp4	20.6 MB
03. Deploying the 'absenteeism_module' - Part II.vtt	8.2 KB
02. Deploying the 'absenteeism_module' - Part I.vtt	5.1 KB
04. Exporting the Obtained Data Set as a .csv.html	1.0 KB
01. Are You Sure You're All Set.html	0.5 KB
/assets/
02. Absenteeism-predictions.csv	2.2 KB
01. Absenteeism-predictions.csv	2.2 KB
/63. Case Study - Analyzing the Predicted Outputs in Tableau/
04. Analyzing Reasons vs Probability in Tableau.mp4	42.2 MB
02. Analyzing Age vs Probability in Tableau.mp4	40.6 MB
06. Analyzing Transportation Expense vs Probability in Tableau.mp4	17.3 MB
02. Analyzing Age vs Probability in Tableau.vtt	10.5 KB
04. Analyzing Reasons vs Probability in Tableau.vtt	9.9 KB
06. Analyzing Transportation Expense vs Probability in Tableau.vtt	7.8 KB
05. EXERCISE - Transportation Expense vs Probability.html	0.6 KB
03. EXERCISE - Reasons vs Probability.html	0.4 KB
01. EXERCISE - Age vs Probability.html	0.4 KB
/assets/
01. Additional-Python-Tools-Solutions.ipynb	26.1 KB
06. Additional-Python-Tools-Solutions.ipynb	26.1 KB
06. Additional-Python-Tools-Lectures.ipynb	13.8 KB
01. Additional-Python-Tools-Lectures.ipynb	13.8 KB
06. Additional-Python-Tools-Exercises.ipynb	11.7 KB
01. Additional-Python-Tools-Exercises.ipynb	11.7 KB
/64. Appendix - Additional Python Tools/
05. List Comprehensions.mp4	45.3 MB
04. Triple Nested For Loops.mp4	34.6 MB
01. Using the .format() Method.mp4	26.9 MB
06. Anonymous (Lambda) Functions.mp4	23.9 MB
02. Iterating Over Range Objects.mp4	13.2 MB
03. Introduction to Nested For Loops.mp4	12.8 MB
05. List Comprehensions.vtt	13.1 KB
01. Using the .format() Method.vtt	13.0 KB
06. Anonymous (Lambda) Functions.vtt	10.7 KB
03. Introduction to Nested For Loops.vtt	8.7 KB
04. Triple Nested For Loops.vtt	8.7 KB
02. Iterating Over Range Objects.vtt	6.6 KB
/assets/
13. Sales-products.csv	155.9 KB
01. Sales-products.csv	155.9 KB
13. pandas-Fundamentals-Solutions.ipynb	121.2 KB
01. pandas-Fundamentals-Solutions.ipynb	121.2 KB
13. Lending-company.csv	115.1 KB
01. Lending-company.csv	115.1 KB
13. pandas-Fundamentals-Exercises.ipynb	31.7 KB
01. pandas-Fundamentals-Exercises.ipynb	31.7 KB
13. pandas-Fundamentals-Lectures.ipynb	21.8 KB
01. pandas-Fundamentals-Lectures.ipynb	21.8 KB
13. Location.csv	13.8 KB
01. Location.csv	13.8 KB
13. Region.csv	10.5 KB
01. Region.csv	10.5 KB
/65. Appendix - pandas Fundamentals/
11. Data Selection in pandas DataFrames.mp4	39.1 MB
12. pandas DataFrames - Indexing with .iloc[].mp4	33.8 MB
10. pandas DataFrames - Common Attributes.mp4	26.9 MB
01. Introduction to pandas Series.mp4	26.2 MB
06. Using .unique() and .nunique().mp4	25.5 MB
05. Parameters and Arguments in pandas.mp4	22.2 MB
13. pandas DataFrames - Indexing with .loc[].mp4	21.7 MB
09. Introduction to pandas DataFrames - Part II.mp4	18.7 MB
07. Using .sort_values().mp4	16.0 MB
03. Working with Methods in Python - Part I.mp4	13.9 MB
08. Introduction to pandas DataFrames - Part I.mp4	13.1 MB
04. Working with Methods in Python - Part II.mp4	9.4 MB
01. Introduction to pandas Series.vtt	11.1 KB
11. Data Selection in pandas DataFrames.vtt	10.8 KB
12. pandas DataFrames - Indexing with .iloc[].vtt	8.5 KB
09. Introduction to pandas DataFrames - Part II.vtt	8.2 KB
08. Introduction to pandas DataFrames - Part I.vtt	7.5 KB
03. Working with Methods in Python - Part I.vtt	7.4 KB
10. pandas DataFrames - Common Attributes.vtt	6.7 KB
06. Using .unique() and .nunique().vtt	6.0 KB
05. Parameters and Arguments in pandas.vtt	5.9 KB
07. Using .sort_values().vtt	5.7 KB
13. pandas DataFrames - Indexing with .loc[].vtt	5.7 KB
04. Working with Methods in Python - Part II.vtt	4.0 KB
02. A Note on Completing the Upcoming Coding Exercises.html	3.0 KB
/assets/
01. 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf	16.3 MB
/66. Bonus Lecture/
01. Bonus Lecture Next Steps.html	4.4 KB
Total files 1506