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Language:
English
Total Size:
9.2 GB
Info Hash:
9A03EAE6885C6EACA8F516880C02705743F8EACA
Added By:
Added:
May 7, 2025, 9:30 a.m.
Stats:
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(Last updated: May 7, 2025, 9:31 a.m.)
| File | Size |
|---|---|
| 01. A Practical Example What You Will Learn in This Course.mp4 | 10.8 MB |
| 01. A Practical Example What You Will Learn in This Course.vtt | 6.8 KB |
| 02. What Does the Course Cover.mp4 | 9.6 MB |
| 02. What Does the Course Cover.vtt | 5.4 KB |
| 03. Download All Resources and Important FAQ.html | 21.3 KB |
| 03. FAQ-The-Data-Science-Course.pdf | 306.1 KB |
| 01. Data Science and Business Buzzwords Why are there so Many.mp4 | 15.6 MB |
| 01. Data Science and Business Buzzwords Why are there so Many.vtt | 7.4 KB |
| 02. What is the difference between Analysis and Analytics.mp4 | 11.2 MB |
| 02. What is the difference between Analysis and Analytics.vtt | 5.1 KB |
| 03. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 | 14.6 MB |
| 03. Business Analytics, Data Analytics, and Data Science An Introduction.vtt | 9.6 KB |
| 04. Continuing with BI, ML, and AI.mp4 | 47.6 MB |
| 04. Continuing with BI, ML, and AI.vtt | 13.1 KB |
| 05. Traditional AI vs. Generative AI.mp4 | 24.5 MB |
| 05. Traditional AI vs. Generative AI.vtt | 6.9 KB |
| 06. More Examples of Generative AI.mp4 | 30.5 MB |
| 06. More Examples of Generative AI.vtt | 6.9 KB |
| 07. A Breakdown of our Data Science Infographic.mp4 | 45.4 MB |
| 07. A Breakdown of our Data Science Infographic.vtt | 5.1 KB |
| 03. 365-DataScience-Diagram.pdf | 323.1 KB |
| 04. 365-DataScience-Diagram.pdf | 323.1 KB |
| 04. 365-DataScience.png | 6.9 MB |
| 07. 365-DataScience.png | 6.9 MB |
| 01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 | 83.5 MB |
| 01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt | 9.2 KB |
| 01. The Reason Behind These Disciplines.mp4 | 46.8 MB |
| 01. The Reason Behind These Disciplines.vtt | 6.5 KB |
| 01. Techniques for Working with Traditional Data.mp4 | 107.2 MB |
| 01. Techniques for Working with Traditional Data.vtt | 11.0 KB |
| 02. Real Life Examples of Traditional Data.mp4 | 18.4 MB |
| 02. Real Life Examples of Traditional Data.vtt | 2.3 KB |
| 03. Techniques for Working with Big Data.mp4 | 62.1 MB |
| 03. Techniques for Working with Big Data.vtt | 5.8 KB |
| 04. Real Life Examples of Big Data.mp4 | 13.0 MB |
| 04. Real Life Examples of Big Data.vtt | 1.9 KB |
| 05. Business Intelligence (BI) Techniques.mp4 | 52.9 MB |
| 05. Business Intelligence (BI) Techniques.vtt | 8.8 KB |
| 06. Real Life Examples of Business Intelligence (BI).mp4 | 24.6 MB |
| 06. Real Life Examples of Business Intelligence (BI).vtt | 2.3 KB |
| 07. Techniques for Working with Traditional Methods.mp4 | 76.0 MB |
| 07. Techniques for Working with Traditional Methods.vtt | 11.5 KB |
| 08. Real Life Examples of Traditional Methods.mp4 | 36.7 MB |
| 08. Real Life Examples of Traditional Methods.vtt | 5.4 KB |
| 09. Machine Learning (ML) Techniques.mp4 | 49.4 MB |
| 09. Machine Learning (ML) Techniques.vtt | 9.3 KB |
| 10. Types of Machine Learning.mp4 | 69.5 MB |
| 10. Types of Machine Learning.vtt | 10.9 KB |
| 11. Evolution and Latest Trends of Machine Learning (ML).mp4 | 27.3 MB |
| 11. Evolution and Latest Trends of Machine Learning (ML).vtt | 7.8 KB |
| 12. Real Life Examples of Machine Learning (ML).mp4 | 27.7 MB |
| 12. Real Life Examples of Machine Learning (ML).vtt | 3.0 KB |
| 01. Necessary Programming Languages and Software Used in Data Science.mp4 | 82.4 MB |
| 01. Necessary Programming Languages and Software Used in Data Science.vtt | 8.0 KB |
| 01. Finding the Job - What to Expect and What to Look for.mp4 | 40.0 MB |
| 01. Finding the Job - What to Expect and What to Look for.vtt | 4.7 KB |
| 01. Debunking Common Misconceptions.mp4 | 58.9 MB |
| 01. Debunking Common Misconceptions.vtt | 5.5 KB |
| 01. The Basic Probability Formula.mp4 | 29.4 MB |
| 01. The Basic Probability Formula.vtt | 9.0 KB |
| 02. Computing Expected Values.mp4 | 45.7 MB |
| 02. Computing Expected Values.vtt | 7.0 KB |
| 03. Frequency.mp4 | 37.4 MB |
| 03. Frequency.vtt | 6.9 KB |
| 04. Events and Their Complements.mp4 | 25.8 MB |
| 04. Events and Their Complements.vtt | 7.1 KB |
| 01. Course-Notes-Basic-Probability.pdf | 371.1 KB |
| 01. Fundamentals of Combinatorics.mp4 | 5.9 MB |
| 01. Fundamentals of Combinatorics.vtt | 1.5 KB |
| 02. Permutations and How to Use Them.mp4 | 17.5 MB |
| 02. Permutations and How to Use Them.vtt | 4.4 KB |
| 03. Simple Operations with Factorials.mp4 | 10.5 MB |
| 03. Simple Operations with Factorials.vtt | 3.4 KB |
| 04. Solving Variations with Repetition.mp4 | 13.9 MB |
| 04. Solving Variations with Repetition.vtt | 3.8 KB |
| 05. Solving Variations without Repetition.mp4 | 18.3 MB |
| 05. Solving Variations without Repetition.vtt | 4.9 KB |
| 06. Solving Combinations.mp4 | 23.6 MB |
| 06. Solving Combinations.vtt | 6.0 KB |
| 07. Symmetry of Combinations.mp4 | 13.7 MB |
| 07. Symmetry of Combinations.vtt | 4.5 KB |
| 08. Solving Combinations with Separate Sample Spaces.mp4 | 20.3 MB |
| 08. Solving Combinations with Separate Sample Spaces.vtt | 4.1 KB |
| 09. Combinatorics in Real-Life The Lottery.mp4 | 16.4 MB |
| 09. Combinatorics in Real-Life The Lottery.vtt | 4.2 KB |
| 10. A Recap of Combinatorics.mp4 | 12.1 MB |
| 10. A Recap of Combinatorics.vtt | 3.8 KB |
| 11. A Practical Example of Combinatorics.mp4 | 80.7 MB |
| 11. A Practical Example of Combinatorics.vtt | 15.2 KB |
| 01. Course-Notes-Combinatorics.pdf | 226.1 KB |
| 06. Combinations-With-Repetition.pdf | 207.4 KB |
| 07. Symmetry-Explained.pdf | 85.0 KB |
| 11. Additional-Exercises-Combinatorics-Solutions.pdf | 245.7 KB |
| 11. Additional-Exercises-Combinatorics.pdf | 106.6 KB |
| 01. Sets and Events.mp4 | 17.7 MB |
| 01. Sets and Events.vtt | 5.5 KB |
| 02. Ways Sets Can Interact.mp4 | 11.3 MB |
| 02. Ways Sets Can Interact.vtt | 4.6 KB |
| 03. Intersection of Sets.mp4 | 11.0 MB |
| 03. Intersection of Sets.vtt | 2.6 KB |
| 04. Union of Sets.mp4 | 24.2 MB |
| 04. Union of Sets.vtt | 6.3 KB |
| 05. Mutually Exclusive Sets.mp4 | 10.6 MB |
| 05. Mutually Exclusive Sets.vtt | 2.8 KB |
| 06. Dependence and Independence of Sets.mp4 | 14.9 MB |
| 06. Dependence and Independence of Sets.vtt | 3.5 KB |
| 07. The Conditional Probability Formula.mp4 | 20.1 MB |
| 07. The Conditional Probability Formula.vtt | 5.9 KB |
| 08. The Law of Total Probability.mp4 | 14.2 MB |
| 08. The Law of Total Probability.vtt | 3.9 KB |
| 09. The Additive Rule.mp4 | 11.1 MB |
| 09. The Additive Rule.vtt | 2.7 KB |
| 10. The Multiplication Law.mp4 | 20.2 MB |
| 10. The Multiplication Law.vtt | 4.7 KB |
| 11. Bayes' Law.mp4 | 21.3 MB |
| 11. Bayes' Law.vtt | 7.7 KB |
| 12. A Practical Example of Bayesian Inference.mp4 | 139.2 MB |
| 12. A Practical Example of Bayesian Inference.vtt | 20.1 KB |
| 01. Course-Notes-Bayesian-Inference.pdf | 386.0 KB |
| 12. Bayesian-Homework-Solutions.pdf | 30.4 KB |
| 12. Bayesian-Homework.pdf | 27.3 KB |
| 12. CDS-2017-2018-Hamilton.pdf | 845.3 KB |
| 01. Fundamentals of Probability Distributions.mp4 | 19.4 MB |
| 01. Fundamentals of Probability Distributions.vtt | 8.4 KB |
| 02. Types of Probability Distributions.mp4 | 35.6 MB |
| 02. Types of Probability Distributions.vtt | 10.4 KB |
| 03. Characteristics of Discrete Distributions.mp4 | 9.4 MB |
| 03. Characteristics of Discrete Distributions.vtt | 2.6 KB |
| 04. Discrete Distributions The Uniform Distribution.mp4 | 10.3 MB |
| 04. Discrete Distributions The Uniform Distribution.vtt | 2.9 KB |
| 05. Discrete Distributions The Bernoulli Distribution.mp4 | 15.1 MB |
| 05. Discrete Distributions The Bernoulli Distribution.vtt | 5.2 KB |
| 06. Discrete Distributions The Binomial Distribution.mp4 | 30.6 MB |
| 06. Discrete Distributions The Binomial Distribution.vtt | 8.8 KB |
| 07. Discrete Distributions The Poisson Distribution.mp4 | 23.9 MB |
| 07. Discrete Distributions The Poisson Distribution.vtt | 7.2 KB |
| 08. Characteristics of Continuous Distributions.mp4 | 21.3 MB |
| 08. Characteristics of Continuous Distributions.vtt | 9.2 KB |
| 09. Continuous Distributions The Normal Distribution.mp4 | 20.0 MB |
| 09. Continuous Distributions The Normal Distribution.vtt | 5.1 KB |
| 10. Continuous Distributions The Standard Normal Distribution.mp4 | 21.1 MB |
| 10. Continuous Distributions The Standard Normal Distribution.vtt | 5.7 KB |
| 11. Continuous Distributions The Students' T Distribution.mp4 | 9.2 MB |
| 11. Continuous Distributions The Students' T Distribution.vtt | 3.2 KB |
| 12. Continuous Distributions The Chi-Squared Distribution.mp4 | 11.2 MB |
| 12. Continuous Distributions The Chi-Squared Distribution.vtt | 3.0 KB |
| 13. Continuous Distributions The Exponential Distribution.mp4 | 16.0 MB |
| 13. Continuous Distributions The Exponential Distribution.vtt | 4.5 KB |
| 14. Continuous Distributions The Logistic Distribution.mp4 | 16.2 MB |
| 14. Continuous Distributions The Logistic Distribution.vtt | 5.4 KB |
| 15. A Practical Example of Probability Distributions.mp4 | 138.3 MB |
| 15. A Practical Example of Probability Distributions.vtt | 21.1 KB |
| 01. Course-Notes-Probability-Distributions.pdf | 463.9 KB |
| 07. Poisson-Expected-Value-and-Variance.pdf | 146.0 KB |
| 08. Solving-Integrals.pdf | 343.9 KB |
| 09. Normal-Distribution-Exp-and-Var.pdf | 144.1 KB |
| 15. Customers-Membership-post.xlsx | 15.6 KB |
| 15. Customers-Membership.xlsx | 9.7 KB |
| 15. Daily-Views-post.xlsx | 20.2 KB |
| 15. Daily-Views.xlsx | 9.5 KB |
| 15. FIFA19-post.csv | 8.6 MB |
| 15. FIFA19.csv | 8.6 MB |
| 01. Probability in Finance.mp4 | 40.3 MB |
| 01. Probability in Finance.vtt | 10.1 KB |
| 02. Probability in Statistics.mp4 | 31.6 MB |
| 02. Probability in Statistics.vtt | 9.1 KB |
| 03. Probability in Data Science.mp4 | 14.2 MB |
| 03. Probability in Data Science.vtt | 7.1 KB |
| 01. Probability-in-Finance-Homework.pdf | 110.7 KB |
| 01. Probability-in-Finance-Solutions.pdf | 184.5 KB |
| 03. Probability-Cheat-Sheet.pdf | 320.3 KB |
| 01. Population and Sample.mp4 | 35.1 MB |
| 01. Population and Sample.vtt | 5.8 KB |
| 01. Course-notes-descriptive-statistics.pdf | 482.2 KB |
| 01. Statistics-Glossary.xlsx | 20.3 KB |
| 01. Types of Data.mp4 | 43.2 MB |
| 01. Types of Data.vtt | 5.8 KB |
| 02. Levels of Measurement.mp4 | 32.2 MB |
| 02. Levels of Measurement.vtt | 4.8 KB |
| 03. Categorical Variables - Visualization Techniques.mp4 | 27.5 MB |
| 03. Categorical Variables - Visualization Techniques.vtt | 6.7 KB |
| 04. Categorical Variables Exercise.html | 81 bytes |
| 05. Numerical Variables - Frequency Distribution Table.mp4 | 17.7 MB |
| 05. Numerical Variables - Frequency Distribution Table.vtt | 4.5 KB |
| 06. Numerical Variables Exercise.html | 81 bytes |
| 07. The Histogram.mp4 | 9.6 MB |
| 07. The Histogram.vtt | 3.4 KB |
| 08. Histogram Exercise.html | 81 bytes |
| 09. Cross Tables and Scatter Plots.mp4 | 19.7 MB |
| 09. Cross Tables and Scatter Plots.vtt | 6.9 KB |
| 10. Cross Tables and Scatter Plots Exercise.html | 81 bytes |
| 11. Mean, median and mode.mp4 | 24.5 MB |
| 11. Mean, median and mode.vtt | 6.0 KB |
| 12. Mean, Median and Mode Exercise.html | 81 bytes |
| 13. Skewness.mp4 | 13.3 MB |
| 13. Skewness.vtt | 3.7 KB |
| 14. Skewness Exercise.html | 81 bytes |
| 15. Variance.mp4 | 23.5 MB |
| 15. Variance.vtt | 8.2 KB |
| 16. Variance Exercise.html | 522 bytes |
| 17. Standard Deviation and Coefficient of Variation.mp4 | 20.1 MB |
| 17. Standard Deviation and Coefficient of Variation.vtt | 6.3 KB |
| 18. Standard Deviation and Coefficient of Variation Exercise.html | 81 bytes |
| 19. Covariance.mp4 | 18.4 MB |
| 19. Covariance.vtt | 5.1 KB |
| 20. Covariance Exercise.html | 81 bytes |
| 21. Correlation Coefficient.mp4 | 19.3 MB |
| 21. Correlation Coefficient.vtt | 5.0 KB |
| 22. Correlation Coefficient Exercise.html | 81 bytes |
| 01. Course-notes-descriptive-statistics.pdf | 482.2 KB |
| 01. Glossary.xlsx | 20.0 KB |
| 03. 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx | 30.8 KB |
| 04. 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx | 41.1 KB |
| 04. 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx | 15.2 KB |
| 04. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf | 289.1 KB |
| 05. 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx | 11.4 KB |
| 06. 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx | 13.2 KB |
| 07. 2.5.The-Histogram-lesson.xlsx | 18.6 KB |
| 08. 2.5.The-Histogram-exercise-solution.xlsx | 17.1 KB |
| 08. 2.5.The-Histogram-exercise.xlsx | 15.5 KB |
| 08. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf | 289.1 KB |
| 09. 2.6.Cross-table-and-scatter-plot.xlsx | 26.1 KB |
| 10. 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx | 40.4 KB |
| 10. 2.6.Cross-table-and-scatter-plot-exercise.xlsx | 16.3 KB |
| 11. 2.7.Mean-median-and-mode-lesson.xlsx | 10.5 KB |
| 12. 2.7.Mean-median-and-mode-exercise-solution.xlsx | 11.4 KB |
| 12. 2.7.Mean-median-and-mode-exercise.xlsx | 10.9 KB |
| 13. 2.8.Skewness-lesson.xlsx | 34.6 KB |
| 14. 2.8.Skewness-exercise-solution.xlsx | 19.8 KB |
| 14. 2.8.Skewness-exercise.xlsx | 9.5 KB |
| 15. 2.9.Variance-lesson.xlsx | 10.1 KB |
| 16. 2.9.Variance-exercise-solution.xlsx | 11.1 KB |
| 16. 2.9.Variance-exercise.xlsx | 10.8 KB |
| 17. 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx | 11.0 KB |
| 18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx | 12.6 KB |
| 18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx | 11.6 KB |
| 19. 2.11.Covariance-lesson.xlsx | 24.9 KB |
| 20. 2.11.Covariance-exercise-solution.xlsx | 29.5 KB |
| 20. 2.11.Covariance-exercise.xlsx | 20.2 KB |
| 22. 2.12.Correlation-exercise-solution.xlsx | 29.5 KB |
| 22. 2.12.Correlation-exercise.xlsx | 29.3 KB |
| 01. Practical Example Descriptive Statistics.mp4 | 130.5 MB |
| 01. Practical Example Descriptive Statistics.vtt | 21.0 KB |
| 02. Practical Example Descriptive Statistics Exercise.html | 81 bytes |
| 01. 2.13.Practical-example.Descriptive-statistics-lesson.xlsx | 146.5 KB |
| 02. 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx | 146.4 KB |
| 02. 2.13.Practical-example.Descriptive-statistics-exercise.xlsx | 120.3 KB |
| 01. Introduction.mp4 | 3.1 MB |
| 01. Introduction.vtt | 1.7 KB |
| 02. What is a Distribution.mp4 | 17.2 MB |
| 02. What is a Distribution.vtt | 5.9 KB |
| 03. The Normal Distribution.mp4 | 13.1 MB |
| 03. The Normal Distribution.vtt | 5.2 KB |
| 04. The Standard Normal Distribution.mp4 | 8.6 MB |
| 04. The Standard Normal Distribution.vtt | 4.0 KB |
| 05. The Standard Normal Distribution Exercise.html | 81 bytes |
| 06. Central Limit Theorem.mp4 | 23.2 MB |
| 06. Central Limit Theorem.vtt | 5.6 KB |
| 07. Standard error.mp4 | 13.5 MB |
| 07. Standard error.vtt | 2.1 KB |
| 08. Estimators and Estimates.mp4 | 27.7 MB |
| 08. Estimators and Estimates.vtt | 4.0 KB |
| 01. Course-notes-inferential-statistics.pdf | 382.3 KB |
| 02. 3.2.What-is-a-distribution-lesson.xlsx | 19.5 KB |
| 02. Course-notes-inferential-statistics.pdf | 382.3 KB |
| 04. 3.4.Standard-normal-distribution-lesson.xlsx | 10.4 KB |
| 05. 3.4.Standard-normal-distribution-exercise-solution.xlsx | 24.0 KB |
| 05. 3.4.Standard-normal-distribution-exercise.xlsx | 12.0 KB |
| 01. What are Confidence Intervals.mp4 | 28.6 MB |
| 01. What are Confidence Intervals.vtt | 3.2 KB |
| 02. Confidence Intervals; Population Variance Known; Z-score.mp4 | 52.2 MB |
| 02. Confidence Intervals; Population Variance Known; Z-score.vtt | 9.6 KB |
| 03. Confidence Intervals; Population Variance Known; Z-score; Exercise.html | 81 bytes |
| 04. Confidence Interval Clarifications.mp4 | 18.9 MB |
| 04. Confidence Interval Clarifications.vtt | 5.6 KB |
| 05. Student's T Distribution.mp4 | 13.7 MB |
| 05. Student's T Distribution.vtt | 4.6 KB |
| 06. Confidence Intervals; Population Variance Unknown; T-score.mp4 | 13.7 MB |
| 06. Confidence Intervals; Population Variance Unknown; T-score.vtt | 5.4 KB |
| 07. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html | 81 bytes |
| 08. Margin of Error.mp4 | 23.1 MB |
| 08. Margin of Error.vtt | 6.4 KB |
| 09. Confidence intervals. Two means. Dependent samples.mp4 | 45.0 MB |
| 09. Confidence intervals. Two means. Dependent samples.vtt | 8.5 KB |
| 10. Confidence intervals. Two means. Dependent samples Exercise.html | 81 bytes |
| 11. Confidence intervals. Two means. Independent Samples (Part 1).mp4 | 12.0 MB |
| 11. Confidence intervals. Two means. Independent Samples (Part 1).vtt | 6.3 KB |
| 12. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html | 81 bytes |
| 13. Confidence intervals. Two means. Independent Samples (Part 2).mp4 | 14.6 MB |
| 13. Confidence intervals. Two means. Independent Samples (Part 2).vtt | 4.7 KB |
| 14. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html | 81 bytes |
| 15. Confidence intervals. Two means. Independent Samples (Part 3).mp4 | 6.9 MB |
| 15. Confidence intervals. Two means. Independent Samples (Part 3).vtt | 2.0 KB |
| 02. 3.9.Population-variance-known-z-score-lesson.xlsx | 11.2 KB |
| 02. 3.9.The-z-table.xlsx | 25.6 KB |
| 03. 3.9.Population-variance-known-z-score-exercise-solution.xlsx | 11.2 KB |
| 03. 3.9.Population-variance-known-z-score-exercise.xlsx | 10.8 KB |
| 03. 3.9.The-z-table.xlsx | 25.6 KB |
| 06. 3.11.Population-variance-unknown-t-score-lesson.xlsx | 10.8 KB |
| 06. 3.11.The-t-table.xlsx | 15.8 KB |
| 07. 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx | 11.1 KB |
| 07. 3.11.Population-variance-unknown-t-score-exercise.xlsx | 10.6 KB |
| 07. 3.11.The-t-table.xlsx | 15.8 KB |
| 09. 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx | 10.5 KB |
| 10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx | 14.2 KB |
| 10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx | 13.7 KB |
| 11. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx | 9.8 KB |
| 12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx | 10.1 KB |
| 12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx | 9.8 KB |
| 13. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx | 9.5 KB |
| 14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx | 9.8 KB |
| 14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx | 9.2 KB |
| 01. Practical Example Inferential Statistics.mp4 | 69.0 MB |
| 01. Practical Example Inferential Statistics.vtt | 13.9 KB |
| 02. Practical Example Inferential Statistics Exercise.html | 81 bytes |
| 01. 3.17.Practical-example.Confidence-intervals-lesson.xlsx | 1.7 MB |
| 02. 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx | 1.8 MB |
| 02. 3.17.Practical-example.Confidence-intervals-exercise.xlsx | 1.7 MB |
| 01. Null vs Alternative Hypothesis.mp4 | 31.9 MB |
| 01. Null vs Alternative Hypothesis.vtt | 7.2 KB |
| 02. Further Reading on Null and Alternative Hypothesis.html | 2.3 KB |
| 03. Rejection Region and Significance Level.mp4 | 38.7 MB |
| 03. Rejection Region and Significance Level.vtt | 8.6 KB |
| 04. Type I Error and Type II Error.mp4 | 15.3 MB |
| 04. Type I Error and Type II Error.vtt | 5.5 KB |
| 05. Test for the Mean. Population Variance Known.mp4 | 36.9 MB |
| 05. Test for the Mean. Population Variance Known.vtt | 8.1 KB |
| 06. Test for the Mean. Population Variance Known Exercise.html | 81 bytes |
| 07. p-value.mp4 | 33.7 MB |
| 07. p-value.vtt | 5.3 KB |
| 08. Test for the Mean. Population Variance Unknown.mp4 | 19.7 MB |
| 08. Test for the Mean. Population Variance Unknown.vtt | 6.0 KB |
| 09. Test for the Mean. Population Variance Unknown Exercise.html | 81 bytes |
| 10. Test for the Mean. Dependent Samples.mp4 | 32.8 MB |
| 10. Test for the Mean. Dependent Samples.vtt | 6.8 KB |
| 11. Test for the Mean. Dependent Samples Exercise.html | 81 bytes |
| 12. Test for the mean. Independent Samples (Part 1).mp4 | 15.4 MB |
| 12. Test for the mean. Independent Samples (Part 1).vtt | 5.5 KB |
| 13. Test for the mean. Independent Samples (Part 1). Exercise.html | 81 bytes |
| 14. Test for the mean. Independent Samples (Part 2).mp4 | 24.4 MB |
| 14. Test for the mean. Independent Samples (Part 2).vtt | 5.4 KB |
| 15. Test for the mean. Independent Samples (Part 2). Exercise.html | 81 bytes |
| 01. Course-notes-hypothesis-testing.pdf | 656.4 KB |
| 03. Course-notes-hypothesis-testing.pdf | 656.4 KB |
| 05. 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx | 11.0 KB |
| 06. 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx | 11.2 KB |
| 06. 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx | 11.0 KB |
| 07. Online-p-value-calculator.pdf | 1.2 MB |
| 08. 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx | 14.5 KB |
| 09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx | 12.6 KB |
| 09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx | 11.3 KB |
| 10. 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx | 9.8 KB |
| 11. 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx | 14.4 KB |
| 11. 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx | 12.8 KB |
| 12. 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx | 9.6 KB |
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| 13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx | 10.8 KB |
| 14. 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx | 9.3 KB |
| 15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx | 11.4 KB |
| 15. 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx | 10.5 KB |
| 01. Practical Example Hypothesis Testing.mp4 | 45.8 MB |
| 01. Practical Example Hypothesis Testing.vtt | 8.6 KB |
| 02. Practical Example Hypothesis Testing Exercise.html | 81 bytes |
| 01. 4.10.Hypothesis-testing-section-practical-example.xlsx | 51.9 KB |
| 02. 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx | 44.3 KB |
| 02. 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx | 43.7 KB |
| 01. Introduction to Programming.mp4 | 14.9 MB |
| 01. Introduction to Programming.vtt | 7.3 KB |
| 02. Why Python.mp4 | 12.2 MB |
| 02. Why Python.vtt | 7.2 KB |
| 03. Why Jupyter.mp4 | 8.0 MB |
| 03. Why Jupyter.vtt | 4.2 KB |
| 04. Installing Python and Jupyter.mp4 | 18.8 MB |
| 04. Installing Python and Jupyter.vtt | 4.9 KB |
| 05. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 | 6.1 MB |
| 05. Understanding Jupyter's Interface - the Notebook Dashboard.vtt | 3.7 KB |
| 06. Prerequisites for Coding in the Jupyter Notebooks.mp4 | 19.0 MB |
| 06. Prerequisites for Coding in the Jupyter Notebooks.vtt | 7.9 KB |
| 01. Introduction-to-Python-Course-Notes.pdf | 2.2 MB |
| 01. Variables.mp4 | 8.9 MB |
| 01. Variables.vtt | 4.8 KB |
| 02. Numbers and Boolean Values in Python.mp4 | 6.6 MB |
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| 03. Python Strings.mp4 | 19.7 MB |
| 03. Python Strings.vtt | 7.7 KB |
| 01. Introduction-to-Python-Course-Notes.pdf | 2.2 MB |
| 01. Variables-Exercise-Py3.ipynb | 2.2 KB |
| 01. Variables-Lecture-Py3.ipynb | 3.6 KB |
| 01. Variables-Solution-Py3.ipynb | 3.8 KB |
| 02. Numbers-and-Boolean-Values-Exercise-Py3.ipynb | 2.3 KB |
| 02. Numbers-and-Boolean-Values-Lecture-Py3.ipynb | 3.4 KB |
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| 03. Strings-Exercise-Py3.ipynb | 2.6 KB |
| 03. Strings-Lecture-Py3.ipynb | 7.6 KB |
| 03. Strings-Solution-Py3.ipynb | 5.5 KB |
| 01. Using Arithmetic Operators in Python.mp4 | 8.6 MB |
| 01. Using Arithmetic Operators in Python.vtt | 4.4 KB |
| 02. The Double Equality Sign.mp4 | 2.7 MB |
| 02. The Double Equality Sign.vtt | 1.9 KB |
| 03. How to Reassign Values.mp4 | 1.9 MB |
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| 04. Add Comments.mp4 | 2.4 MB |
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| 05. Understanding Line Continuation.mp4 | 1.2 MB |
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| 06. Indexing Elements.mp4 | 2.4 MB |
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| 07. Structuring with Indentation.mp4 | 2.8 MB |
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| 01. Arithmetic-Operators-Exercise-Py3.ipynb | 2.6 KB |
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| 02. The-Double-Equality-Sign-Exercise-Py3.ipynb | 838 bytes |
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| 03. Reassign-Values-Exercise-Py3.ipynb | 1.7 KB |
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| 05. Line-Continuation-Exercise-Py3.ipynb | 1.1 KB |
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| 06. Indexing-Elements-Exercise-Py3.ipynb | 1.3 KB |
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| 01. Comparison Operators.mp4 | 4.2 MB |
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| 02. Logical and Identity Operators.mp4 | 19.0 MB |
| 02. Logical and Identity Operators.vtt | 6.0 KB |
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| 02. Logical-and-Identity-Operators-Lecture-Py3.ipynb | 5.9 KB |
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| 01. The IF Statement.mp4 | 6.7 MB |
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| 02. The ELSE Statement.mp4 | 6.0 MB |
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| 03. The ELIF Statement.mp4 | 14.2 MB |
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| 04. A Note on Boolean Values.mp4 | 4.2 MB |
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| 01. Introduction-to-the-If-Statement-Exercise-Py3.ipynb | 1.5 KB |
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| 03. Else-If-for-Brief-Elif-Solution-Py3.ipynb | 2.4 KB |
| 04. A-Note-on-Boolean-Values-Lecture-Py3.ipynb | 791 bytes |
| 01. Defining a Function in Python.mp4 | 3.2 MB |
| 01. Defining a Function in Python.vtt | 2.6 KB |
| 02. How to Create a Function with a Parameter.mp4 | 10.0 MB |
| 02. How to Create a Function with a Parameter.vtt | 4.5 KB |
| 03. Defining a Function in Python - Part II.mp4 | 6.5 MB |
| 03. Defining a Function in Python - Part II.vtt | 3.1 KB |
| 04. How to Use a Function within a Function.mp4 | 3.2 MB |
| 04. How to Use a Function within a Function.vtt | 2.1 KB |
| 05. Conditional Statements and Functions.mp4 | 6.0 MB |
| 05. Conditional Statements and Functions.vtt | 3.6 KB |
| 06. Functions Containing a Few Arguments.mp4 | 2.8 MB |
| 06. Functions Containing a Few Arguments.vtt | 1.4 KB |
| 07. Built-in Functions in Python.mp4 | 10.2 MB |
| 07. Built-in Functions in Python.vtt | 4.3 KB |
| 01. Defining-a-Function-in-Python-Lecture-Py3.ipynb | 868 bytes |
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| 02. Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb | 1.6 KB |
| 02. Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb | 1.8 KB |
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| 03. Another-Way-to-Define-a-Function-Solution-Py3.ipynb | 2.0 KB |
| 04. 0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb | 1.0 KB |
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| 05. Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb | 1.1 KB |
| 05. Combining-Conditional-Statements-and-Functions-Lecture-Py3.ipynb | 1.3 KB |
| 05. Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb | 1.6 KB |
| 06. Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb | 1.7 KB |
| 07. Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb | 3.7 KB |
| 07. Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb | 4.5 KB |
| 07. Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb | 5.5 KB |
| 01. Lists.mp4 | 23.0 MB |
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| 02. Using Methods.mp4 | 30.4 MB |
| 02. Using Methods.vtt | 8.7 KB |
| 03. List Slicing.mp4 | 19.2 MB |
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| 04. Tuples.mp4 | 18.2 MB |
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| 05. Dictionaries.mp4 | 32.4 MB |
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| 01. Lists-Exercise-Py3.ipynb | 2.1 KB |
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| 02. Help-Yourself-with-Methods-Lecture-Py3.ipynb | 4.4 KB |
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| 04. Tuples-Exercise-Py3.ipynb | 2.1 KB |
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| 04. Tuples-Solution-Py3.ipynb | 4.6 KB |
| 05. Dictionaries-Exercise-Py3.ipynb | 2.9 KB |
| 05. Dictionaries-Lecture-Py3.ipynb | 4.4 KB |
| 05. Dictionaries-Solution-Py3.ipynb | 6.2 KB |
| 01. For Loops.mp4 | 13.0 MB |
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| 02. While Loops and Incrementing.mp4 | 20.2 MB |
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| 03. Lists with the range() Function.mp4 | 16.1 MB |
| 03. Lists with the range() Function.vtt | 8.6 KB |
| 04. Conditional Statements and Loops.mp4 | 17.3 MB |
| 04. Conditional Statements and Loops.vtt | 8.0 KB |
| 05. Conditional Statements, Functions, and Loops.mp4 | 4.3 MB |
| 05. Conditional Statements, Functions, and Loops.vtt | 2.5 KB |
| 06. How to Iterate over Dictionaries.mp4 | 18.4 MB |
| 06. How to Iterate over Dictionaries.vtt | 7.8 KB |
| 01. For-Loops-Exercise-Py3.ipynb | 1.3 KB |
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| 02. While-Loops-and-Incrementing-Exercise-Py3.ipynb | 1.1 KB |
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| 03. Create-Lists-with-the-range-Function-Exercise-Py3.ipynb | 1.5 KB |
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| 04. Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb | 2.1 KB |
| 04. Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb | 1.9 KB |
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| 05. All-In-Solution-Py3.ipynb | 1.9 KB |
| 06. Iterating-over-Dictionaries-Exercise-Py3.ipynb | 2.2 KB |
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| 01. Object Oriented Programming.mp4 | 8.7 MB |
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| 02. Modules and Packages.mp4 | 2.1 MB |
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| 03. What is the Standard Library.mp4 | 5.1 MB |
| 03. What is the Standard Library.vtt | 3.9 KB |
| 04. Importing Modules in Python.mp4 | 9.9 MB |
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| 01. Introduction to Regression Analysis.mp4 | 3.6 MB |
| 01. Introduction to Regression Analysis.vtt | 2.3 KB |
| 01. Course-notes-regression-analysis.pdf | 312.2 KB |
| 01. The Linear Regression Model.mp4 | 13.5 MB |
| 01. The Linear Regression Model.vtt | 8.1 KB |
| 02. Correlation vs Regression.mp4 | 3.8 MB |
| 02. Correlation vs Regression.vtt | 2.2 KB |
| 03. Geometrical Representation of the Linear Regression Model.mp4 | 2.3 MB |
| 03. Geometrical Representation of the Linear Regression Model.vtt | 1.7 KB |
| 04. Python Packages Installation.mp4 | 23.7 MB |
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| 05. First Regression in Python.mp4 | 29.6 MB |
| 05. First Regression in Python.vtt | 8.2 KB |
| 06. First Regression in Python Exercise.html | 1.3 KB |
| 07. Using Seaborn for Graphs.mp4 | 7.4 MB |
| 07. Using Seaborn for Graphs.vtt | 1.6 KB |
| 08. How to Interpret the Regression Table.mp4 | 28.7 MB |
| 08. How to Interpret the Regression Table.vtt | 6.3 KB |
| 09. Decomposition of Variability.mp4 | 8.8 MB |
| 09. Decomposition of Variability.vtt | 4.5 KB |
| 10. What is the OLS.mp4 | 22.5 MB |
| 10. What is the OLS.vtt | 3.8 KB |
| 11. R-Squared.mp4 | 11.2 MB |
| 11. R-Squared.vtt | 6.8 KB |
| 01. Course-notes-regression-analysis.pdf | 312.2 KB |
| 05. 1.01.Simple-linear-regression.csv | 922 bytes |
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| 06. real-estate-price-size.csv | 1.9 KB |
| 06. Simple-Linear-Regression-Exercise-Solution.ipynb | 3.6 KB |
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| 01. Multiple Linear Regression.mp4 | 5.7 MB |
| 01. Multiple Linear Regression.vtt | 3.5 KB |
| 02. Adjusted R-Squared.mp4 | 34.2 MB |
| 02. Adjusted R-Squared.vtt | 7.5 KB |
| 03. Multiple Linear Regression Exercise.html | 76 bytes |
| 04. Test for Significance of the Model (F-Test).mp4 | 7.2 MB |
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| 05. OLS Assumptions.mp4 | 5.3 MB |
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| 06. A1 Linearity.mp4 | 3.6 MB |
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| 07. A2 No Endogeneity.mp4 | 9.2 MB |
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| 08. A3 Normality and Homoscedasticity.mp4 | 27.4 MB |
| 08. A3 Normality and Homoscedasticity.vtt | 7.0 KB |
| 09. A4 No Autocorrelation.mp4 | 7.9 MB |
| 09. A4 No Autocorrelation.vtt | 5.0 KB |
| 10. A5 No Multicollinearity.mp4 | 7.6 MB |
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| 11. Dealing with Categorical Data - Dummy Variables.mp4 | 22.6 MB |
| 11. Dealing with Categorical Data - Dummy Variables.vtt | 9.5 KB |
| 12. Dealing with Categorical Data - Dummy Variables.html | 76 bytes |
| 13. Making Predictions with the Linear Regression.mp4 | 16.3 MB |
| 13. Making Predictions with the Linear Regression.vtt | 4.4 KB |
| 02. 1.02.Multiple-linear-regression.csv | 1.1 KB |
| 02. Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb | 2.8 KB |
| 02. Multiple-linear-regression-and-Adjusted-R-squared.ipynb | 2.1 KB |
| 03. Multiple-Linear-Regression-Exercise-Solution.ipynb | 13.4 KB |
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| 12. Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb | 18.0 KB |
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| 01. What is sklearn and How is it Different from Other Packages.mp4 | 8.5 MB |
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| 02. How are we Going to Approach this Section.mp4 | 5.3 MB |
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| 03. Simple Linear Regression with sklearn.mp4 | 27.4 MB |
| 03. Simple Linear Regression with sklearn.vtt | 7.6 KB |
| 04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 | 22.3 MB |
| 04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt | 6.8 KB |
| 05. A Note on Normalization.html | 733 bytes |
| 06. Simple Linear Regression with sklearn - Exercise.html | 76 bytes |
| 07. Multiple Linear Regression with sklearn.mp4 | 8.3 MB |
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| 08. Calculating the Adjusted R-Squared in sklearn.mp4 | 16.9 MB |
| 08. Calculating the Adjusted R-Squared in sklearn.vtt | 6.7 KB |
| 09. Calculating the Adjusted R-Squared in sklearn - Exercise.html | 76 bytes |
| 10. Feature Selection (F-regression).mp4 | 20.5 MB |
| 10. Feature Selection (F-regression).vtt | 6.9 KB |
| 11. A Note on Calculation of P-values with sklearn.html | 372 bytes |
| 12. Creating a Summary Table with P-values.mp4 | 6.4 MB |
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| 13. Multiple Linear Regression - Exercise.html | 76 bytes |
| 14. Feature Scaling (Standardization).mp4 | 20.4 MB |
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| 15. Feature Selection through Standardization of Weights.mp4 | 24.5 MB |
| 15. Feature Selection through Standardization of Weights.vtt | 7.6 KB |
| 16. Predicting with the Standardized Coefficients.mp4 | 20.4 MB |
| 16. Predicting with the Standardized Coefficients.vtt | 5.6 KB |
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| 18. Underfitting and Overfitting.mp4 | 5.8 MB |
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| 19. Train - Test Split Explained.mp4 | 35.6 MB |
| 19. Train - Test Split Explained.vtt | 9.7 KB |
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| 09. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb | 10.3 KB |
| 09. sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb | 9.8 KB |
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| 10. sklearn-Feature-Selection-with-F-regression-with-comments.ipynb | 13.0 KB |
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| 12. sklearn-Multiple-Linear-Regression-Summary-Table-with-comments.ipynb | 16.6 KB |
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| 17. sklearn-Feature-Scaling-Exercise-Solution.ipynb | 16.3 KB |
| 17. sklearn-Feature-Scaling-Exercise.ipynb | 6.1 KB |
| 19. sklearn-Train-Test-Split-with-comments.ipynb | 9.0 KB |
| 19. sklearn-Train-Test-Split.ipynb | 7.2 KB |
| 01. Practical Example Linear Regression (Part 1).mp4 | 84.7 MB |
| 01. Practical Example Linear Regression (Part 1).vtt | 14.9 KB |
| 02. Practical Example Linear Regression (Part 2).mp4 | 31.9 MB |
| 02. Practical Example Linear Regression (Part 2).vtt | 8.3 KB |
| 03. A Note on Multicollinearity.html | 849 bytes |
| 04. Practical Example Linear Regression (Part 3).mp4 | 16.7 MB |
| 04. Practical Example Linear Regression (Part 3).vtt | 4.5 KB |
| 05. Dummies and Variance Inflation Factor - Exercise.html | 76 bytes |
| 06. Practical Example Linear Regression (Part 4).mp4 | 39.4 MB |
| 06. Practical Example Linear Regression (Part 4).vtt | 11.9 KB |
| 07. Dummy Variables - Exercise.html | 713 bytes |
| 08. Practical Example Linear Regression (Part 5).mp4 | 50.4 MB |
| 08. Practical Example Linear Regression (Part 5).vtt | 11.1 KB |
| 09. Linear Regression - Exercise.html | 503 bytes |
| 01. 1.04.Real-life-example.csv | 219.8 KB |
| 01. sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb | 171.4 KB |
| 01. sklearn-Linear-Regression-Practical-Example-Part-1.ipynb | 166.9 KB |
| 02. 1.04.Real-life-example.csv | 219.8 KB |
| 02. sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb | 335.6 KB |
| 02. sklearn-Linear-Regression-Practical-Example-Part-2.ipynb | 328.7 KB |
| 04. sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb | 351.5 KB |
| 04. sklearn-Linear-Regression-Practical-Example-Part-3.ipynb | 343.6 KB |
| 05. 1.04.Real-life-example.csv | 219.8 KB |
| 05. sklearn-Dummies-and-VIF-Exercise-Solution.ipynb | 370.2 KB |
| 05. sklearn-Dummies-and-VIF-Exercise.ipynb | 344.6 KB |
| 06. 1.04.Real-life-example.csv | 219.8 KB |
| 06. sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb | 407.6 KB |
| 06. sklearn-Linear-Regression-Practical-Example-Part-4.ipynb | 397.2 KB |
| 08. 1.04.Real-life-example.csv | 219.8 KB |
| 08. sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb | 711.0 KB |
| 08. sklearn-Linear-Regression-Practical-Example-Part-5.ipynb | 698.4 KB |
| 01. Introduction to Logistic Regression.mp4 | 5.9 MB |
| 01. Introduction to Logistic Regression.vtt | 1.8 KB |
| 02. A Simple Example in Python.mp4 | 21.9 MB |
| 02. A Simple Example in Python.vtt | 5.9 KB |
| 03. Logistic vs Logit Function.mp4 | 23.7 MB |
| 03. Logistic vs Logit Function.vtt | 5.1 KB |
| 04. Building a Logistic Regression.mp4 | 8.6 MB |
| 04. Building a Logistic Regression.vtt | 3.5 KB |
| 05. Building a Logistic Regression - Exercise.html | 87 bytes |
| 06. An Invaluable Coding Tip.mp4 | 18.8 MB |
| 06. An Invaluable Coding Tip.vtt | 3.1 KB |
| 07. Understanding Logistic Regression Tables.mp4 | 14.6 MB |
| 07. Understanding Logistic Regression Tables.vtt | 5.6 KB |
| 08. Understanding Logistic Regression Tables - Exercise.html | 87 bytes |
| 09. What do the Odds Actually Mean.mp4 | 11.4 MB |
| 09. What do the Odds Actually Mean.vtt | 4.4 KB |
| 10. Binary Predictors in a Logistic Regression.mp4 | 24.9 MB |
| 10. Binary Predictors in a Logistic Regression.vtt | 5.3 KB |
| 11. Binary Predictors in a Logistic Regression - Exercise.html | 87 bytes |
| 12. Calculating the Accuracy of the Model.mp4 | 20.3 MB |
| 12. Calculating the Accuracy of the Model.vtt | 4.3 KB |
| 13. Calculating the Accuracy of the Model.html | 87 bytes |
| 14. Underfitting and Overfitting.mp4 | 7.5 MB |
| 14. Underfitting and Overfitting.vtt | 5.2 KB |
| 15. Testing the Model.mp4 | 21.6 MB |
| 15. Testing the Model.vtt | 6.5 KB |
| 16. Testing the Model - Exercise.html | 87 bytes |
| 01. Course-Notes-Logistic-Regression.pdf | 335.2 KB |
| 02. 2.01.Admittance.csv | 1.6 KB |
| 02. Admittance-with-comments.ipynb | 5.3 KB |
| 02. Admittance.ipynb | 3.5 KB |
| 02. Course-Notes-Logistic-Regression.pdf | 335.2 KB |
| 04. Admittance-regression-summary-error.ipynb | 2.5 KB |
| 04. Admittance-regression-tables-fixed-error.ipynb | 4.1 KB |
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| 05. Building-a-Logistic-Regression-Exercise.ipynb | 2.9 KB |
| 05. Building-a-Logistic-Regression-Solution.ipynb | 4.4 KB |
| 05. Example-bank-data.csv | 6.2 KB |
| 08. Bank-data.csv | 19.5 KB |
| 08. Understanding-Logistic-Regression-Tables-Exercise.ipynb | 3.2 KB |
| 08. Understanding-Logistic-Regression-Tables-Solution.ipynb | 4.8 KB |
| 10. 2.02.Binary-predictors.csv | 2.6 KB |
| 10. Binary-predictors.ipynb | 2.4 KB |
| 11. Bank-data.csv | 19.5 KB |
| 11. Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb | 2.5 KB |
| 11. Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb | 4.5 KB |
| 12. Accuracy-with-comments.ipynb | 11.7 KB |
| 12. Accuracy.ipynb | 3.6 KB |
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| 13. Calculating-the-Accuracy-of-the-Model-Exercise.ipynb | 5.4 KB |
| 13. Calculating-the-Accuracy-of-the-Model-Solution.ipynb | 81.2 KB |
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| 15. Testing-the-model-with-comments.ipynb | 7.6 KB |
| 15. Testing-the-model.ipynb | 5.8 KB |
| 16. Bank-data-testing.csv | 8.3 KB |
| 16. Bank-data.csv | 19.5 KB |
| 16. Testing-the-Model-Exercise.ipynb | 6.8 KB |
| 16. Testing-the-Model-Solution.ipynb | 111.1 KB |
| 01. Introduction to Cluster Analysis.mp4 | 14.5 MB |
| 01. Introduction to Cluster Analysis.vtt | 5.0 KB |
| 02. Some Examples of Clusters.mp4 | 35.9 MB |
| 02. Some Examples of Clusters.vtt | 6.3 KB |
| 03. Difference between Classification and Clustering.mp4 | 9.7 MB |
| 03. Difference between Classification and Clustering.vtt | 3.6 KB |
| 04. Math Prerequisites.mp4 | 5.3 MB |
| 04. Math Prerequisites.vtt | 4.4 KB |
| 01. Course-Notes-Cluster-Analysis.pdf | 208.7 KB |
| 02. Course-Notes-Cluster-Analysis.pdf | 208.7 KB |
| 01. K-Means Clustering.mp4 | 10.8 MB |
| 01. K-Means Clustering.vtt | 6.6 KB |
| 02. A Simple Example of Clustering.mp4 | 34.2 MB |
| 02. A Simple Example of Clustering.vtt | 9.7 KB |
| 03. A Simple Example of Clustering - Exercise.html | 87 bytes |
| 04. Clustering Categorical Data.mp4 | 10.4 MB |
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| 05. Clustering Categorical Data - Exercise.html | 87 bytes |
| 06. How to Choose the Number of Clusters.mp4 | 26.9 MB |
| 06. How to Choose the Number of Clusters.vtt | 7.6 KB |
| 07. How to Choose the Number of Clusters - Exercise.html | 87 bytes |
| 08. Pros and Cons of K-Means Clustering.mp4 | 11.1 MB |
| 08. Pros and Cons of K-Means Clustering.vtt | 4.6 KB |
| 09. To Standardize or not to Standardize.mp4 | 10.9 MB |
| 09. To Standardize or not to Standardize.vtt | 6.3 KB |
| 10. Relationship between Clustering and Regression.mp4 | 3.5 MB |
| 10. Relationship between Clustering and Regression.vtt | 2.3 KB |
| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 | 28.0 MB |
| 11. Market Segmentation with Cluster Analysis (Part 1).vtt | 7.5 KB |
| 12. Market Segmentation with Cluster Analysis (Part 2).mp4 | 34.1 MB |
| 12. Market Segmentation with Cluster Analysis (Part 2).vtt | 9.1 KB |
| 13. How is Clustering Useful.mp4 | 37.4 MB |
| 13. How is Clustering Useful.vtt | 6.8 KB |
| 14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html | 87 bytes |
| 15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html | 87 bytes |
| 02. 3.01.Country-clusters.csv | 200 bytes |
| 02. Country-clusters-with-comments.ipynb | 5.8 KB |
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| 03. A-Simple-Example-of-Clustering-Exercise.ipynb | 3.6 KB |
| 03. A-Simple-Example-of-Clustering-Solution.ipynb | 4.6 KB |
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| 04. Categorical-data-with-comments.ipynb | 5.6 KB |
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| 05. Categorical.csv | 10.3 KB |
| 05. Clustering-Categorical-Data-Exercise.ipynb | 3.8 KB |
| 05. Clustering-Categorical-Data-Solution.ipynb | 4.9 KB |
| 06. Selecting-the-number-of-clusters-with-comments.ipynb | 7.5 KB |
| 06. Selecting-the-number-of-clusters.ipynb | 4.5 KB |
| 07. Countries-exercise.csv | 8.3 KB |
| 07. How-to-Choose-the-Number-of-Clusters-Exercise.ipynb | 5.5 KB |
| 07. How-to-Choose-the-Number-of-Clusters-Solution.ipynb | 8.5 KB |
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| 11. Market-segmentation-example.ipynb | 3.8 KB |
| 12. Market-segmentation-example-Part2-with-comments.ipynb | 6.8 KB |
| 12. Market-segmentation-example-Part2.ipynb | 4.7 KB |
| 14. iris-dataset.csv | 2.4 KB |
| 14. Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb | 4.5 KB |
| 14. Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb | 7.4 KB |
| 15. iris-dataset.csv | 2.4 KB |
| 15. iris-with-answers.csv | 3.6 KB |
| 15. Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb | 10.7 KB |
| 15. Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb | 15.3 KB |
| 01. Types of Clustering.mp4 | 9.0 MB |
| 01. Types of Clustering.vtt | 5.1 KB |
| 02. Dendrogram.mp4 | 18.3 MB |
| 02. Dendrogram.vtt | 7.6 KB |
| 03. Heatmaps.mp4 | 18.5 MB |
| 03. Heatmaps.vtt | 6.2 KB |
| 03. Country-clusters-standardized.csv | 244 bytes |
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| 03. Heatmaps.ipynb | 1.8 KB |
| 01. Traditional data science methods and the role of ChatGPT.mp4 | 26.2 MB |
| 01. Traditional data science methods and the role of ChatGPT.vtt | 7.2 KB |
| 02. How to install ChatGPT.mp4 | 5.2 MB |
| 02. How to install ChatGPT.vtt | 2.0 KB |
| 03. How ChatGPT can boost your productivity.mp4 | 5.4 MB |
| 03. How ChatGPT can boost your productivity.vtt | 2.4 KB |
| 04. Data Preprocessing with ChatGPT.mp4 | 28.7 MB |
| 04. Data Preprocessing with ChatGPT.vtt | 6.4 KB |
| 05. First attempt at machine learning with ChatGPT.mp4 | 36.7 MB |
| 05. First attempt at machine learning with ChatGPT.vtt | 6.4 KB |
| 06. Analyzing a client database with ChatGPT in Python.mp4 | 21.6 MB |
| 06. Analyzing a client database with ChatGPT in Python.vtt | 5.2 KB |
| 07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp4 | 15.2 MB |
| 07. Analyzing a client database with ChatGPT in Python – analyzing top products.vtt | 5.2 KB |
| 08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp4 | 27.2 MB |
| 08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.vtt | 5.8 KB |
| 09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4 | 21.6 MB |
| 09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.vtt | 7.4 KB |
| 10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4 | 33.7 MB |
| 10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.vtt | 7.6 KB |
| 11. Assignment 1.html | 1.6 KB |
| 12. Hypothesis testing with ChatGPT.mp4 | 14.4 MB |
| 12. Hypothesis testing with ChatGPT.vtt | 5.6 KB |
| 13. Marvels comic book database Intro to Regular Expressions (RegEx).mp4 | 15.0 MB |
| 13. Marvels comic book database Intro to Regular Expressions (RegEx).vtt | 2.7 KB |
| 14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4 | 33.1 MB |
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| 15. Assignment 2.html | 1.6 KB |
| 16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4 | 17.3 MB |
| 16. Algorithm recommendation Movie Database Analysis with ChatGPT.vtt | 4.4 KB |
| 17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp4 | 17.8 MB |
| 17. Algorithm recommendation recommendation engine for movies with ChatGPT.vtt | 6.4 KB |
| 18. Ethical principles in data and AI utilization.mp4 | 14.7 MB |
| 18. Ethical principles in data and AI utilization.vtt | 4.4 KB |
| 19. Using ChatGPT for ethical considerations.mp4 | 33.5 MB |
| 19. Using ChatGPT for ethical considerations.vtt | 7.5 KB |
| 04. Data-Preprocessing-Medical-Data.ipynb | 7.5 KB |
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| 05. diagnosis-mapping.csv | 90 bytes |
| 05. Medical-Data-ML-Attempt.ipynb | 4.4 KB |
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| 06. products.csv | 1.8 KB |
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| 08. Furniture-store-data-analysis.ipynb | 52.4 KB |
| 10. Properties-analysis.ipynb | 286.5 KB |
| 10. properties.csv | 2.7 KB |
| 12. Students-Hypothesis-Testing.ipynb | 5.6 KB |
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| 14. Marvel-Comics-Reg-Ex.ipynb | 29.5 KB |
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| 19. users.csv | 3.5 KB |
| Marvel_Comics.csv | 13.0 MB |
| movies_metadata.csv | 32.8 MB |
| 01. Intro to the Case Study.mp4 | 10.4 MB |
| 01. Intro to the Case Study.vtt | 3.7 KB |
| 02. The Naive Bayes Algorithm.mp4 | 42.1 MB |
| 02. The Naive Bayes Algorithm.vtt | 6.1 KB |
| 03. Tokenization and Vectorization.mp4 | 15.8 MB |
| 03. Tokenization and Vectorization.vtt | 7.9 KB |
| 04. Imbalanced Data Sets.mp4 | 6.6 MB |
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| 05. Overcome Imbalanced Data in Machine Learning.mp4 | 14.6 MB |
| 05. Overcome Imbalanced Data in Machine Learning.vtt | 5.0 KB |
| 06. Loading the Dataset and Preprocessing.mp4 | 14.8 MB |
| 06. Loading the Dataset and Preprocessing.vtt | 3.7 KB |
| 07. Optimizing User Reviews Data Preprocessing & EDA.mp4 | 18.7 MB |
| 07. Optimizing User Reviews Data Preprocessing & EDA.vtt | 6.0 KB |
| 08. Reg Ex for Analyzing Text Review Data.mp4 | 16.2 MB |
| 08. Reg Ex for Analyzing Text Review Data.vtt | 5.1 KB |
| 09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp4 | 13.9 MB |
| 09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.vtt | 5.4 KB |
| 10. Machine Learning with Naïve Bayes (First Attempt).mp4 | 28.1 MB |
| 10. Machine Learning with Naïve Bayes (First Attempt).vtt | 8.4 KB |
| 11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp4 | 18.9 MB |
| 11. Machine Learning with Naïve Bayes – converting the problem to a binary one.vtt | 6.7 KB |
| 12. Testing the Model on New Data.mp4 | 20.8 MB |
| 12. Testing the Model on New Data.vtt | 6.9 KB |
| 12. 365-User-Reviews-Naive-Bayes-Sentiment-Analysis.ipynb | 1.7 MB |
| 12. user-courses-review-test-set.csv | 19.6 KB |
| 01. What is a Matrix.mp4 | 11.9 MB |
| 01. What is a Matrix.vtt | 4.6 KB |
| 02. Scalars and Vectors.mp4 | 8.5 MB |
| 02. Scalars and Vectors.vtt | 4.0 KB |
| 03. Linear Algebra and Geometry.mp4 | 13.7 MB |
| 03. Linear Algebra and Geometry.vtt | 4.1 KB |
| 04. Arrays in Python - A Convenient Way To Represent Matrices.mp4 | 19.0 MB |
| 04. Arrays in Python - A Convenient Way To Represent Matrices.vtt | 6.2 KB |
| 05. What is a Tensor.mp4 | 15.5 MB |
| 05. What is a Tensor.vtt | 3.8 KB |
| 06. Addition and Subtraction of Matrices.mp4 | 22.1 MB |
| 06. Addition and Subtraction of Matrices.vtt | 4.2 KB |
| 07. Errors when Adding Matrices.mp4 | 5.8 MB |
| 07. Errors when Adding Matrices.vtt | 2.7 KB |
| 08. Transpose of a Matrix.mp4 | 14.2 MB |
| 08. Transpose of a Matrix.vtt | 5.6 KB |
| 09. Dot Product.mp4 | 12.8 MB |
| 09. Dot Product.vtt | 4.3 KB |
| 10. Dot Product of Matrices.mp4 | 34.3 MB |
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| 11. Why is Linear Algebra Useful.mp4 | 88.5 MB |
| 11. Why is Linear Algebra Useful.vtt | 11.5 KB |
| 04. Scalars-Vectors-and-Matrices.ipynb | 4.5 KB |
| 05. Tensors.ipynb | 2.1 KB |
| 06. Adding-and-subtracting-matrices.ipynb | 3.2 KB |
| 07. Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb | 3.2 KB |
| 08. Tranpose-of-a-matrix.ipynb | 2.9 KB |
| 09. Dot-product.ipynb | 2.1 KB |
| 10. Dot-product-Part-2.ipynb | 3.6 KB |
| 01. What to Expect from this Part.mp4 | 11.7 MB |
| 01. What to Expect from this Part.vtt | 4.8 KB |
| 01. Introduction to Neural Networks.mp4 | 10.5 MB |
| 01. Introduction to Neural Networks.vtt | 6.2 KB |
| 02. Training the Model.mp4 | 7.7 MB |
| 02. Training the Model.vtt | 4.7 KB |
| 03. Types of Machine Learning.mp4 | 13.1 MB |
| 03. Types of Machine Learning.vtt | 5.5 KB |
| 04. The Linear Model (Linear Algebraic Version).mp4 | 8.0 MB |
| 04. The Linear Model (Linear Algebraic Version).vtt | 3.7 KB |
| 05. The Linear Model with Multiple Inputs.mp4 | 7.9 MB |
| 05. The Linear Model with Multiple Inputs.vtt | 2.8 KB |
| 06. The Linear model with Multiple Inputs and Multiple Outputs.mp4 | 16.6 MB |
| 06. The Linear model with Multiple Inputs and Multiple Outputs.vtt | 4.8 KB |
| 07. Graphical Representation of Simple Neural Networks.mp4 | 7.8 MB |
| 07. Graphical Representation of Simple Neural Networks.vtt | 2.7 KB |
| 08. What is the Objective Function.mp4 | 6.2 MB |
| 08. What is the Objective Function.vtt | 2.3 KB |
| 09. Common Objective Functions L2-norm Loss.mp4 | 5.5 MB |
| 09. Common Objective Functions L2-norm Loss.vtt | 2.9 KB |
| 10. Common Objective Functions Cross-Entropy Loss.mp4 | 9.8 MB |
| 10. Common Objective Functions Cross-Entropy Loss.vtt | 5.4 KB |
| 11. Optimization Algorithm 1-Parameter Gradient Descent.mp4 | 23.6 MB |
| 11. Optimization Algorithm 1-Parameter Gradient Descent.vtt | 8.8 KB |
| 12. Optimization Algorithm n-Parameter Gradient Descent.mp4 | 16.8 MB |
| 12. Optimization Algorithm n-Parameter Gradient Descent.vtt | 7.8 KB |
| 01. Course-Notes-Section-2.pdf | 578.1 KB |
| 02. Course-Notes-Section-2.pdf | 578.1 KB |
| 11. GD-function-example.xlsx | 42.3 KB |
| 01. Basic NN Example (Part 1).mp4 | 9.3 MB |
| 01. Basic NN Example (Part 1).vtt | 4.5 KB |
| 02. Basic NN Example (Part 2).mp4 | 15.2 MB |
| 02. Basic NN Example (Part 2).vtt | 6.7 KB |
| 03. Basic NN Example (Part 3).mp4 | 15.7 MB |
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| 04. Basic NN Example (Part 4).mp4 | 40.0 MB |
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| 05. Minimal-example-Exercise-3.a.Solution.ipynb | 67.9 KB |
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| 05. Minimal-example-Exercise-6-Solution.ipynb | 61.8 KB |
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| 01. How to Install TensorFlow 2.0.mp4 | 27.3 MB |
| 01. How to Install TensorFlow 2.0.vtt | 6.6 KB |
| 02. TensorFlow Outline and Comparison with Other Libraries.mp4 | 15.3 MB |
| 02. TensorFlow Outline and Comparison with Other Libraries.vtt | 5.5 KB |
| 03. TensorFlow 1 vs TensorFlow 2.mp4 | 15.3 MB |
| 03. TensorFlow 1 vs TensorFlow 2.vtt | 4.0 KB |
| 04. A Note on TensorFlow 2 Syntax.mp4 | 4.6 MB |
| 04. A Note on TensorFlow 2 Syntax.vtt | 1.4 KB |
| 05. Types of File Formats Supporting TensorFlow.mp4 | 8.9 MB |
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| 06. Outlining the Model with TensorFlow 2.mp4 | 27.0 MB |
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| 07. Interpreting the Result and Extracting the Weights and Bias.mp4 | 25.9 MB |
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| 08. Customizing a TensorFlow 2 Model.mp4 | 16.8 MB |
| 08. Customizing a TensorFlow 2 Model.vtt | 4.3 KB |
| 09. Basic NN with TensorFlow Exercises.html | 1.3 KB |
| 01. Shortcuts-for-Jupyter.pdf | 619.2 KB |
| 05. TensorFlow-Minimal-example-Part1.ipynb | 1.7 KB |
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| 08. TensorFlow-Minimal-example-complete.ipynb | 76.9 KB |
| 09. TensorFlow-Minimal-example-All-exercises.ipynb | 83.6 KB |
| 09. TensorFlow-Minimal-example-Exercise-1-Solution.ipynb | 28.0 KB |
| 09. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb | 83.7 KB |
| 09. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb | 77.5 KB |
| 09. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb | 84.4 KB |
| 01. What is a Layer.mp4 | 5.2 MB |
| 01. What is a Layer.vtt | 2.7 KB |
| 02. What is a Deep Net.mp4 | 9.1 MB |
| 02. What is a Deep Net.vtt | 3.3 KB |
| 03. Digging into a Deep Net.mp4 | 23.7 MB |
| 03. Digging into a Deep Net.vtt | 6.9 KB |
| 04. Non-Linearities and their Purpose.mp4 | 22.5 MB |
| 04. Non-Linearities and their Purpose.vtt | 4.2 KB |
| 05. Activation Functions.mp4 | 8.8 MB |
| 05. Activation Functions.vtt | 5.3 KB |
| 06. Activation Functions Softmax Activation.mp4 | 8.7 MB |
| 06. Activation Functions Softmax Activation.vtt | 4.5 KB |
| 07. Backpropagation.mp4 | 20.3 MB |
| 07. Backpropagation.vtt | 4.6 KB |
| 08. Backpropagation Picture.mp4 | 8.1 MB |
| 08. Backpropagation Picture.vtt | 3.8 KB |
| 09. Backpropagation - A Peek into the Mathematics of Optimization.html | 543 bytes |
| 01. Course-Notes-Section-6.pdf | 936.4 KB |
| 02. Course-Notes-Section-6.pdf | 936.4 KB |
| 09. Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf | 182.4 KB |
| 01. What is Overfitting.mp4 | 10.8 MB |
| 01. What is Overfitting.vtt | 5.9 KB |
| 02. Underfitting and Overfitting for Classification.mp4 | 14.0 MB |
| 02. Underfitting and Overfitting for Classification.vtt | 2.8 KB |
| 03. What is Validation.mp4 | 8.4 MB |
| 03. What is Validation.vtt | 5.0 KB |
| 04. Training, Validation, and Test Datasets.mp4 | 9.4 MB |
| 04. Training, Validation, and Test Datasets.vtt | 3.4 KB |
| 05. N-Fold Cross Validation.mp4 | 6.2 MB |
| 05. N-Fold Cross Validation.vtt | 4.4 KB |
| 06. Early Stopping or When to Stop Training.mp4 | 10.3 MB |
| 06. Early Stopping or When to Stop Training.vtt | 7.0 KB |
| 01. What is Initialization.mp4 | 8.9 MB |
| 01. What is Initialization.vtt | 3.7 KB |
| 02. Types of Simple Initializations.mp4 | 5.7 MB |
| 02. Types of Simple Initializations.vtt | 3.8 KB |
| 03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 | 5.5 MB |
| 03. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt | 3.8 KB |
| 01. Stochastic Gradient Descent.mp4 | 7.8 MB |
| 01. Stochastic Gradient Descent.vtt | 4.8 KB |
| 02. Problems with Gradient Descent.mp4 | 3.7 MB |
| 02. Problems with Gradient Descent.vtt | 3.0 KB |
| 03. Momentum.mp4 | 5.2 MB |
| 03. Momentum.vtt | 3.6 KB |
| 04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 | 17.5 MB |
| 04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt | 6.3 KB |
| 05. Learning Rate Schedules Visualized.mp4 | 3.2 MB |
| 05. Learning Rate Schedules Visualized.vtt | 2.2 KB |
| 06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 | 8.5 MB |
| 06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt | 5.6 KB |
| 07. Adam (Adaptive Moment Estimation).mp4 | 7.1 MB |
| 07. Adam (Adaptive Moment Estimation).vtt | 3.4 KB |
| 01. Preprocessing Introduction.mp4 | 9.2 MB |
| 01. Preprocessing Introduction.vtt | 4.1 KB |
| 02. Types of Basic Preprocessing.mp4 | 3.2 MB |
| 02. Types of Basic Preprocessing.vtt | 1.9 KB |
| 03. Standardization.mp4 | 12.1 MB |
| 03. Standardization.vtt | 6.1 KB |
| 04. Preprocessing Categorical Data.mp4 | 5.4 MB |
| 04. Preprocessing Categorical Data.vtt | 2.8 KB |
| 05. Binary and One-Hot Encoding.mp4 | 8.5 MB |
| 05. Binary and One-Hot Encoding.vtt | 5.3 KB |
| 01. MNIST The Dataset.mp4 | 4.5 MB |
| 01. MNIST The Dataset.vtt | 3.6 KB |
| 02. MNIST How to Tackle the MNIST.mp4 | 7.9 MB |
| 02. MNIST How to Tackle the MNIST.vtt | 3.6 KB |
| 03. MNIST Importing the Relevant Packages and Loading the Data.mp4 | 12.2 MB |
| 03. MNIST Importing the Relevant Packages and Loading the Data.vtt | 3.0 KB |
| 04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 | 22.9 MB |
| 04. MNIST Preprocess the Data - Create a Validation Set and Scale It.vtt | 6.5 KB |
| 05. MNIST Preprocess the Data - Scale the Test Data - Exercise.html | 79 bytes |
| 06. MNIST Preprocess the Data - Shuffle and Batch.mp4 | 32.7 MB |
| 06. MNIST Preprocess the Data - Shuffle and Batch.vtt | 9.6 KB |
| 07. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html | 79 bytes |
| 08. MNIST Outline the Model.mp4 | 22.1 MB |
| 08. MNIST Outline the Model.vtt | 7.2 KB |
| 09. MNIST Select the Loss and the Optimizer.mp4 | 10.7 MB |
| 09. MNIST Select the Loss and the Optimizer.vtt | 3.0 KB |
| 10. MNIST Learning.mp4 | 31.0 MB |
| 10. MNIST Learning.vtt | 8.0 KB |
| 11. MNIST - Exercises.html | 2.0 KB |
| 12. MNIST Testing the Model.mp4 | 22.6 MB |
| 12. MNIST Testing the Model.vtt | 6.0 KB |
| 03. TensorFlow-MNIST-Part1-with-comments.ipynb | 4.0 KB |
| 05. TensorFlow-MNIST-Part2-with-comments.ipynb | 6.4 KB |
| 07. TensorFlow-MNIST-Part3-with-comments.ipynb | 8.6 KB |
| 08. TensorFlow-MNIST-Part4-with-comments.ipynb | 10.5 KB |
| 09. TensorFlow-MNIST-Part5-with-comments.ipynb | 11.0 KB |
| 10. TensorFlow-MNIST-Part6-with-comments.ipynb | 12.5 KB |
| 11. 1.TensorFlow-MNIST-Width-Solution.ipynb | 14.8 KB |
| 11. 2.TensorFlow-MNIST-Depth-Solution.ipynb | 15.3 KB |
| 11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb | 15.3 KB |
| 11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb | 15.1 KB |
| 11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb | 14.7 KB |
| 11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb | 15.1 KB |
| 11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb | 15.2 KB |
| 11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb | 20.6 KB |
| 11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb | 15.8 KB |
| 11. TensorFlow-MNIST-All-Exercises.ipynb | 16.7 KB |
| 11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb | 15.0 KB |
| 12. TensorFlow-MNIST-complete-with-comments.ipynb | 14.5 KB |
| 12. TensorFlow-MNIST-complete.ipynb | 6.8 KB |
| 01. Business Case Exploring the Dataset and Identifying Predictors.mp4 | 51.3 MB |
| 01. Business Case Exploring the Dataset and Identifying Predictors.vtt | 11.0 KB |
| 02. Business Case Outlining the Solution.mp4 | 3.0 MB |
| 02. Business Case Outlining the Solution.vtt | 1.9 KB |
| 03. Business Case Balancing the Dataset.mp4 | 22.3 MB |
| 03. Business Case Balancing the Dataset.vtt | 4.3 KB |
| 04. Business Case Preprocessing the Data.mp4 | 73.8 MB |
| 04. Business Case Preprocessing the Data.vtt | 13.6 KB |
| 05. Business Case Preprocessing the Data - Exercise.html | 370 bytes |
| 06. Business Case Load the Preprocessed Data.mp4 | 13.8 MB |
| 06. Business Case Load the Preprocessed Data.vtt | 4.7 KB |
| 07. Business Case Load the Preprocessed Data - Exercise.html | 79 bytes |
| 08. Business Case Learning and Interpreting the Result.mp4 | 29.4 MB |
| 08. Business Case Learning and Interpreting the Result.vtt | 6.3 KB |
| 09. Business Case Setting an Early Stopping Mechanism.mp4 | 43.8 MB |
| 09. Business Case Setting an Early Stopping Mechanism.vtt | 8.1 KB |
| 10. Setting an Early Stopping Mechanism - Exercise.html | 192 bytes |
| 11. Business Case Testing the Model.mp4 | 8.2 MB |
| 11. Business Case Testing the Model.vtt | 2.1 KB |
| 12. Business Case Final Exercise.html | 433 bytes |
| 01. Audiobooks-data.csv | 710.8 KB |
| 04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb | 11.2 KB |
| 04. TensorFlow-Audiobooks-Preprocessing.ipynb | 5.6 KB |
| 05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb | 10.0 KB |
| 05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb | 8.6 KB |
| 07. TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb | 4.6 KB |
| 08. TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb | 19.7 KB |
| 09. TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb | 10.1 KB |
| 11. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb | 12.0 KB |
| 12. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb | 12.0 KB |
| 01. Summary on What You've Learned.mp4 | 9.8 MB |
| 01. Summary on What You've Learned.vtt | 5.5 KB |
| 02. What's Further out there in terms of Machine Learning.mp4 | 4.8 MB |
| 02. What's Further out there in terms of Machine Learning.vtt | 2.7 KB |
| 03. DeepMind and Deep Learning.html | 1.1 KB |
| 04. An overview of CNNs.mp4 | 13.4 MB |
| 04. An overview of CNNs.vtt | 6.4 KB |
| 05. An Overview of RNNs.mp4 | 7.0 MB |
| 05. An Overview of RNNs.vtt | 4.0 KB |
| 06. An Overview of non-NN Approaches.mp4 | 16.1 MB |
| 06. An Overview of non-NN Approaches.vtt | 5.7 KB |
| 01. READ ME!!!!.html | 564 bytes |
| 02. How to Install TensorFlow 1.mp4 | 5.0 MB |
| 02. How to Install TensorFlow 1.vtt | 3.4 KB |
| 03. A Note on Installing Packages in Anaconda.html | 2.3 KB |
| 04. TensorFlow Intro.mp4 | 16.9 MB |
| 04. TensorFlow Intro.vtt | 5.3 KB |
| 05. Actual Introduction to TensorFlow.mp4 | 9.0 MB |
| 05. Actual Introduction to TensorFlow.vtt | 2.3 KB |
| 06. Types of File Formats, supporting Tensors.mp4 | 8.9 MB |
| 06. Types of File Formats, supporting Tensors.vtt | 3.4 KB |
| 07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 | 17.7 MB |
| 07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt | 8.0 KB |
| 08. Basic NN Example with TF Loss Function and Gradient Descent.mp4 | 13.6 MB |
| 08. Basic NN Example with TF Loss Function and Gradient Descent.vtt | 4.9 KB |
| 09. Basic NN Example with TF Model Output.mp4 | 17.1 MB |
| 09. Basic NN Example with TF Model Output.vtt | 7.9 KB |
| 10. Basic NN Example with TF Exercises.html | 1.6 KB |
| 05. Shortcuts-for-Jupyter.pdf | 619.2 KB |
| 06. 5.3.TensorFlow-Minimal-example-Part-1.ipynb | 3.4 KB |
| 07. 5.4.TensorFlow-Minimal-example-Part-2.ipynb | 6.2 KB |
| 08. 5.5.TensorFlow-Minimal-example-Part-3.ipynb | 8.6 KB |
| 09. 5.6.TensorFlow-Minimal-example-complete.ipynb | 12.1 KB |
| 10. TensorFlow-Minimal-Example-All-Exercises.ipynb | 14.0 KB |
| 10. TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb | 23.6 KB |
| 10. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb | 25.5 KB |
| 10. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb | 25.5 KB |
| 10. TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb | 50.0 KB |
| 10. TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb | 21.7 KB |
| 10. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb | 26.7 KB |
| 10. TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb | 27.0 KB |
| 01. MNIST What is the MNIST Dataset.mp4 | 4.8 MB |
| 01. MNIST What is the MNIST Dataset.vtt | 3.6 KB |
| 02. MNIST How to Tackle the MNIST.mp4 | 8.0 MB |
| 02. MNIST How to Tackle the MNIST.vtt | 3.8 KB |
| 03. MNIST Relevant Packages.mp4 | 11.2 MB |
| 03. MNIST Relevant Packages.vtt | 2.2 KB |
| 04. MNIST Model Outline.mp4 | 34.7 MB |
| 04. MNIST Model Outline.vtt | 9.2 KB |
| 05. MNIST Loss and Optimization Algorithm.mp4 | 15.8 MB |
| 05. MNIST Loss and Optimization Algorithm.vtt | 3.6 KB |
| 06. Calculating the Accuracy of the Model.mp4 | 24.4 MB |
| 06. Calculating the Accuracy of the Model.vtt | 5.3 KB |
| 07. MNIST Batching and Early Stopping.mp4 | 9.5 MB |
| 07. MNIST Batching and Early Stopping.vtt | 2.8 KB |
| 08. MNIST Learning.mp4 | 31.8 MB |
| 08. MNIST Learning.vtt | 10.5 KB |
| 09. MNIST Results and Testing.mp4 | 38.1 MB |
| 09. MNIST Results and Testing.vtt | 8.2 KB |
| 10. MNIST Exercises.html | 2.2 KB |
| 11. MNIST Solutions.html | 2.2 KB |
| 03. 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb | 3.9 KB |
| 04. 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb | 6.1 KB |
| 05. 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb | 7.3 KB |
| 06. 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb | 7.9 KB |
| 07. 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb | 8.5 KB |
| 08. 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb | 11.5 KB |
| 09. 12.9.TensorFlow-MNIST-with-comments.ipynb | 13.0 KB |
| 10. TensorFlow-MNIST-Exercises-All.ipynb | 15.5 KB |
| 11. 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb | 14.0 KB |
| 11. 1.TensorFlow-MNIST-Width-Solution.ipynb | 14.0 KB |
| 11. 2.TensorFlow-MNIST-Depth-Solution.ipynb | 14.9 KB |
| 11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb | 16.8 KB |
| 11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb | 14.3 KB |
| 11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb | 13.9 KB |
| 11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb | 14.3 KB |
| 11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb | 14.2 KB |
| 11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb | 14.1 KB |
| 11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb | 15.2 KB |
| 11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb | 17.7 KB |
| 01. Business Case Getting Acquainted with the Dataset.mp4 | 60.3 MB |
| 01. Business Case Getting Acquainted with the Dataset.vtt | 11.0 KB |
| 02. Business Case Outlining the Solution.mp4 | 4.2 MB |
| 02. Business Case Outlining the Solution.vtt | 2.6 KB |
| 03. The Importance of Working with a Balanced Dataset.mp4 | 27.3 MB |
| 03. The Importance of Working with a Balanced Dataset.vtt | 4.4 KB |
| 04. Business Case Preprocessing.mp4 | 74.4 MB |
| 04. Business Case Preprocessing.vtt | 13.6 KB |
| 05. Business Case Preprocessing Exercise.html | 389 bytes |
| 06. Creating a Data Provider.mp4 | 56.3 MB |
| 06. Creating a Data Provider.vtt | 8.3 KB |
| 07. Business Case Model Outline.mp4 | 42.5 MB |
| 07. Business Case Model Outline.vtt | 7.2 KB |
| 08. Business Case Optimization.mp4 | 26.9 MB |
| 08. Business Case Optimization.vtt | 6.9 KB |
| 09. Business Case Interpretation.mp4 | 18.6 MB |
| 09. Business Case Interpretation.vtt | 3.1 KB |
| 10. Business Case Testing the Model.mp4 | 4.4 MB |
| 10. Business Case Testing the Model.vtt | 2.7 KB |
| 11. Business Case A Comment on the Homework.mp4 | 20.6 MB |
| 11. Business Case A Comment on the Homework.vtt | 5.4 KB |
| 12. Business Case Final Exercise.html | 443 bytes |
| 01. Audiobooks-data.csv | 710.8 KB |
| 03. Audiobooks-data.csv | 710.8 KB |
| 04. Audiobooks-data.csv | 710.8 KB |
| 04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb | 11.2 KB |
| 04. TensorFlow-Audiobooks-Preprocessing.ipynb | 5.6 KB |
| 05. Audiobooks-data.csv | 710.8 KB |
| 05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb | 10.0 KB |
| 05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb | 8.6 KB |
| 07. TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb | 10.3 KB |
| 07. TensorFlow-Audiobooks-Outlining-the-model.ipynb | 9.4 KB |
| 08. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb | 12.7 KB |
| 08. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb | 10.6 KB |
| 09. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb | 12.7 KB |
| 09. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb | 10.6 KB |
| 11. Audiobooks-data.csv | 710.8 KB |
| 11. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb | 14.4 KB |
| 11. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb | 11.2 KB |
| 12. Audiobooks-data.csv | 710.8 KB |
| 12. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb | 14.4 KB |
| 12. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb | 11.2 KB |
| 01. What are Data, Servers, Clients, Requests, and Responses.mp4 | 19.5 MB |
| 01. What are Data, Servers, Clients, Requests, and Responses.vtt | 6.3 KB |
| 02. What are Data Connectivity, APIs, and Endpoints.mp4 | 60.2 MB |
| 02. What are Data Connectivity, APIs, and Endpoints.vtt | 9.2 KB |
| 03. Taking a Closer Look at APIs.mp4 | 24.5 MB |
| 03. Taking a Closer Look at APIs.vtt | 10.9 KB |
| 04. Communication between Software Products through Text Files.mp4 | 17.5 MB |
| 04. Communication between Software Products through Text Files.vtt | 5.8 KB |
| 05. Software Integration - Explained.mp4 | 16.0 MB |
| 05. Software Integration - Explained.vtt | 7.0 KB |
| 01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 | 19.7 MB |
| 01. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt | 5.6 KB |
| 02. The Business Task.mp4 | 11.3 MB |
| 02. The Business Task.vtt | 4.1 KB |
| 03. Introducing the Data Set.mp4 | 24.2 MB |
| 03. Introducing the Data Set.vtt | 4.3 KB |
| 01. What to Expect from the Following Sections.html | 2.5 KB |
| 02. Importing the Absenteeism Data in Python.mp4 | 19.5 MB |
| 02. Importing the Absenteeism Data in Python.vtt | 4.0 KB |
| 03. Checking the Content of the Data Set.mp4 | 54.0 MB |
| 03. Checking the Content of the Data Set.vtt | 7.1 KB |
| 04. Introduction to Terms with Multiple Meanings.mp4 | 18.0 MB |
| 04. Introduction to Terms with Multiple Meanings.vtt | 4.3 KB |
| 05. What's Regression Analysis - a Quick Refresher.html | 2.8 KB |
| 06. Using a Statistical Approach towards the Solution to the Exercise.mp4 | 9.9 MB |
| 06. Using a Statistical Approach towards the Solution to the Exercise.vtt | 3.0 KB |
| 07. Dropping a Column from a DataFrame in Python.mp4 | 41.2 MB |
| 07. Dropping a Column from a DataFrame in Python.vtt | 8.2 KB |
| 08. EXERCISE - Dropping a Column from a DataFrame in Python.html | 870 bytes |
| 09. SOLUTION - Dropping a Column from a DataFrame in Python.html | 114 bytes |
| 10. Analyzing the Reasons for Absence.mp4 | 27.6 MB |
| 10. Analyzing the Reasons for Absence.vtt | 6.0 KB |
| 11. Obtaining Dummies from a Single Feature.mp4 | 69.8 MB |
| 11. Obtaining Dummies from a Single Feature.vtt | 10.6 KB |
| 12. EXERCISE - Obtaining Dummies from a Single Feature.html | 129 bytes |
| 13. SOLUTION - Obtaining Dummies from a Single Feature.html | 117 bytes |
| 14. Dropping a Dummy Variable from the Data Set.html | 2.3 KB |
| 15. More on Dummy Variables A Statistical Perspective.mp4 | 5.8 MB |
| 15. More on Dummy Variables A Statistical Perspective.vtt | 1.7 KB |
| 16. Classifying the Various Reasons for Absence.mp4 | 51.3 MB |
| 16. Classifying the Various Reasons for Absence.vtt | 10.5 KB |
| 17. Using .concat() in Python.mp4 | 27.3 MB |
| 17. Using .concat() in Python.vtt | 5.2 KB |
| 18. EXERCISE - Using .concat() in Python.html | 189 bytes |
| 19. SOLUTION - Using .concat() in Python.html | 143 bytes |
| 20. Reordering Columns in a Pandas DataFrame in Python.mp4 | 10.0 MB |
| 20. Reordering Columns in a Pandas DataFrame in Python.vtt | 1.9 KB |
| 21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html | 167 bytes |
| 22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html | 478 bytes |
| 23. Creating Checkpoints while Coding in Jupyter.mp4 | 17.3 MB |
| 23. Creating Checkpoints while Coding in Jupyter.vtt | 3.7 KB |
| 24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html | 137 bytes |
| 25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html | 118 bytes |
| 26. Analyzing the Dates from the Initial Data Set.mp4 | 40.1 MB |
| 26. Analyzing the Dates from the Initial Data Set.vtt | 8.9 KB |
| 27. Extracting the Month Value from the Date Column.mp4 | 33.9 MB |
| 27. Extracting the Month Value from the Date Column.vtt | 8.0 KB |
| 28. Extracting the Day of the Week from the Date Column.mp4 | 19.1 MB |
| 28. Extracting the Day of the Week from the Date Column.vtt | 4.8 KB |
| 29. EXERCISE - Removing the Date Column.html | 1.2 KB |
| 30. Analyzing Several Straightforward Columns for this Exercise.mp4 | 14.3 MB |
| 30. Analyzing Several Straightforward Columns for this Exercise.vtt | 4.6 KB |
| 31. Working on Education, Children, and Pets.mp4 | 27.0 MB |
| 31. Working on Education, Children, and Pets.vtt | 6.0 KB |
| 32. Final Remarks of this Section.mp4 | 13.5 MB |
| 32. Final Remarks of this Section.vtt | 2.7 KB |
| 33. A Note on Exporting Your Data as a .csv File.html | 883 bytes |
| 01. Absenteeism-data.csv | 32.0 KB |
| 01. data-preprocessing-homework.pdf | 134.5 KB |
| 01. df-preprocessed.csv | 29.1 KB |
| 23. Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb | 4.8 KB |
| 29. Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb | 7.3 KB |
| 29. Absenteeism-Exercise-Preprocessing-LECTURES.ipynb | 7.6 MB |
| 29. Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb | 8.3 KB |
| 32. Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb | 4.1 KB |
| 32. Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb | 8.5 KB |
| 01. Exploring the Problem with a Machine Learning Mindset.mp4 | 13.0 MB |
| 01. Exploring the Problem with a Machine Learning Mindset.vtt | 4.8 KB |
| 02. Creating the Targets for the Logistic Regression.mp4 | 32.4 MB |
| 02. Creating the Targets for the Logistic Regression.vtt | 8.7 KB |
| 03. Selecting the Inputs for the Logistic Regression.mp4 | 8.7 MB |
| 03. Selecting the Inputs for the Logistic Regression.vtt | 3.6 KB |
| 04. Standardizing the Data.mp4 | 15.1 MB |
| 04. Standardizing the Data.vtt | 4.3 KB |
| 05. Splitting the Data for Training and Testing.mp4 | 36.1 MB |
| 05. Splitting the Data for Training and Testing.vtt | 8.5 KB |
| 06. Fitting the Model and Assessing its Accuracy.mp4 | 15.2 MB |
| 06. Fitting the Model and Assessing its Accuracy.vtt | 7.3 KB |
| 07. Creating a Summary Table with the Coefficients and Intercept.mp4 | 27.0 MB |
| 07. Creating a Summary Table with the Coefficients and Intercept.vtt | 6.4 KB |
| 08. Interpreting the Coefficients for Our Problem.mp4 | 41.1 MB |
| 08. Interpreting the Coefficients for Our Problem.vtt | 8.5 KB |
| 09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 | 16.9 MB |
| 09. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt | 5.2 KB |
| 10. Interpreting the Coefficients of the Logistic Regression.mp4 | 15.2 MB |
| 10. Interpreting the Coefficients of the Logistic Regression.vtt | 7.5 KB |
| 11. Backward Elimination or How to Simplify Your Model.mp4 | 31.8 MB |
| 11. Backward Elimination or How to Simplify Your Model.vtt | 5.4 KB |
| 12. Testing the Model We Created.mp4 | 31.6 MB |
| 12. Testing the Model We Created.vtt | 6.5 KB |
| 13. Saving the Model and Preparing it for Deployment.mp4 | 25.5 MB |
| 13. Saving the Model and Preparing it for Deployment.vtt | 5.8 KB |
| 14. ARTICLE - A Note on 'pickling'.html | 2.1 KB |
| 15. EXERCISE - Saving the Model (and Scaler).html | 284 bytes |
| 16. Preparing the Deployment of the Model through a Module.mp4 | 28.6 MB |
| 16. Preparing the Deployment of the Model through a Module.vtt | 5.9 KB |
| 01. Absenteeism-preprocessed.csv | 29.1 KB |
| 01. Are You Sure You're All Set.html | 519 bytes |
| 02. Deploying the 'absenteeism_module' - Part I.mp4 | 19.7 MB |
| 02. Deploying the 'absenteeism_module' - Part I.vtt | 5.0 KB |
| 03. Deploying the 'absenteeism_module' - Part II.mp4 | 45.1 MB |
| 03. Deploying the 'absenteeism_module' - Part II.vtt | 8.0 KB |
| 04. Exporting the Obtained Data Set as a .csv.html | 998 bytes |
| 01. Absenteeism-Exercise-Integration.ipynb | 62.4 KB |
| 01. absenteeism-module.py | 6.6 KB |
| 01. Absenteeism-new-data.csv | 1.9 KB |
| 01. model | 1.0 KB |
| 01. scaler | 1.9 KB |
| 04. Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb | 973 bytes |
| 01. EXERCISE - Age vs Probability.html | 385 bytes |
| 02. Analyzing Age vs Probability in Tableau.mp4 | 38.7 MB |
| 02. Analyzing Age vs Probability in Tableau.vtt | 10.2 KB |
| 03. EXERCISE - Reasons vs Probability.html | 397 bytes |
| 04. Analyzing Reasons vs Probability in Tableau.mp4 | 40.3 MB |
| 04. Analyzing Reasons vs Probability in Tableau.vtt | 9.7 KB |
| 05. EXERCISE - Transportation Expense vs Probability.html | 553 bytes |
| 06. Analyzing Transportation Expense vs Probability in Tableau.mp4 | 16.5 MB |
| 06. Analyzing Transportation Expense vs Probability in Tableau.vtt | 7.6 KB |
| 01. Absenteeism-predictions.csv | 2.1 KB |
| 02. Absenteeism-predictions.csv | 2.1 KB |
| 01. Using the .format() Method.mp4 | 25.7 MB |
| 01. Using the .format() Method.vtt | 12.7 KB |
| 02. Iterating Over Range Objects.mp4 | 12.6 MB |
| 02. Iterating Over Range Objects.vtt | 6.4 KB |
| 03. Introduction to Nested For Loops.mp4 | 12.2 MB |
| 03. Introduction to Nested For Loops.vtt | 8.5 KB |
| 04. Triple Nested For Loops.mp4 | 33.0 MB |
| 04. Triple Nested For Loops.vtt | 8.5 KB |
| 05. List Comprehensions.mp4 | 43.2 MB |
| 05. List Comprehensions.vtt | 12.8 KB |
| 06. Anonymous (Lambda) Functions.mp4 | 22.8 MB |
| 06. Anonymous (Lambda) Functions.vtt | 10.5 KB |
| 01. Additional-Python-Tools-Exercises.ipynb | 11.4 KB |
| 01. Additional-Python-Tools-Lectures.ipynb | 13.5 KB |
| 01. Additional-Python-Tools-Solutions.ipynb | 25.5 KB |
| 06. Additional-Python-Tools-Exercises.ipynb | 11.4 KB |
| 06. Additional-Python-Tools-Lectures.ipynb | 13.5 KB |
| 06. Additional-Python-Tools-Solutions.ipynb | 25.5 KB |
| 01. Introduction to pandas Series.mp4 | 25.0 MB |
| 01. Introduction to pandas Series.vtt | 10.8 KB |
| 02. A Note on Completing the Upcoming Coding Exercises.html | 3.0 KB |
| 03. Working with Methods in Python - Part I.mp4 | 13.2 MB |
| 03. Working with Methods in Python - Part I.vtt | 7.2 KB |
| 04. Working with Methods in Python - Part II.mp4 | 9.0 MB |
| 04. Working with Methods in Python - Part II.vtt | 3.9 KB |
| 05. Parameters and Arguments in pandas.mp4 | 21.1 MB |
| 05. Parameters and Arguments in pandas.vtt | 5.8 KB |
| 06. Using .unique() and .nunique().mp4 | 24.3 MB |
| 06. Using .unique() and .nunique().vtt | 5.8 KB |
| 07. Using .sort_values().mp4 | 15.2 MB |
| 07. Using .sort_values().vtt | 5.6 KB |
| 08. Introduction to pandas DataFrames - Part I.mp4 | 12.5 MB |
| 08. Introduction to pandas DataFrames - Part I.vtt | 7.3 KB |
| 09. Introduction to pandas DataFrames - Part II.mp4 | 17.8 MB |
| 09. Introduction to pandas DataFrames - Part II.vtt | 8.0 KB |
| 10. pandas DataFrames - Common Attributes.mp4 | 25.6 MB |
| 10. pandas DataFrames - Common Attributes.vtt | 6.6 KB |
| 11. Data Selection in pandas DataFrames.mp4 | 37.3 MB |
| 11. Data Selection in pandas DataFrames.vtt | 10.5 KB |
| 12. pandas DataFrames - Indexing with .iloc[].mp4 | 32.2 MB |
| 12. pandas DataFrames - Indexing with .iloc[].vtt | 8.3 KB |
| 13. pandas DataFrames - Indexing with .loc[].mp4 | 20.7 MB |
| 13. pandas DataFrames - Indexing with .loc[].vtt | 5.6 KB |
| 01. Lending-company.csv | 112.4 KB |
| 01. Location.csv | 13.5 KB |
| 01. pandas-Fundamentals-Exercises.ipynb | 31.0 KB |
| 01. pandas-Fundamentals-Lectures.ipynb | 21.3 KB |
| 01. pandas-Fundamentals-Solutions.ipynb | 118.3 KB |
| 01. Region.csv | 10.2 KB |
| 01. Sales-products.csv | 152.3 KB |
| 13. Lending-company.csv | 112.4 KB |
| 13. Location.csv | 13.5 KB |
| 13. pandas-Fundamentals-Exercises.ipynb | 31.0 KB |
| 13. pandas-Fundamentals-Lectures.ipynb | 21.3 KB |
| 13. pandas-Fundamentals-Solutions.ipynb | 118.3 KB |
| 13. Region.csv | 10.2 KB |
| 13. Sales-products.csv | 152.3 KB |
| 01. Bonus Lecture Next Steps.html | 4.3 KB |
| 01. 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf | 15.6 MB |
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31.7 MB
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2023-07-01
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| Uploaded by FreeCourseWeb | Size 9.3 MB | Health [ 10 /1 ] | Added 2023-07-01 |
NOTE
SOURCE: The Data Science Course Complete Data Science Bootcamp 2025 Dec
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