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Udemy - The Data Science Course 2020 Complete Data Science Bootcamp
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Udemy - The Data Science Course 2020 Complete Data Science Bootcamp (Size: 15.69 GB)
| 1. Part 1 Introduction | |||
| 1. A Practical Example What You Will Learn in This Course.mp4 | 49.03 MB | ||
| 1. A Practical Example What You Will Learn in This Course.srt | 6.37 KB | ||
| 2. What Does the Course Cover.mp4 | 62.26 MB | ||
| 2. What Does the Course Cover.srt | 5.08 KB | ||
| 3. Download All Resources and Important FAQ.html | 21.35 KB | ||
| 3.1 FAQ_The_Data_Science_Course.pdf.pdf | 306.1 KB | ||
| 3.2 Download All Resources.html | 134 B | ||
| 10. Probability - Combinatorics | |||
| 1. Fundamentals of Combinatorics.mp4 | 16.21 MB | ||
| 1. Fundamentals of Combinatorics.srt | 1.3 KB | ||
| 1.1 Course Notes - Combinatorics.pdf.pdf | 226.12 KB | ||
| 10. Solving Variations without Repetition.html | 165 B | ||
| 11. Solving Combinations.mp4 | 57.34 MB | ||
| 11. Solving Combinations.srt | 5.61 KB | ||
| 11.1 Combinations With Repetition.pdf.pdf | 207.41 KB | ||
| 12. Solving Combinations.html | 165 B | ||
| 13. Symmetry of Combinations.mp4 | 40.31 MB | ||
| 13. Symmetry of Combinations.srt | 4.3 KB | ||
| 13.1 Symmetry Explained.pdf.pdf | 85.04 KB | ||
| 14. Symmetry of Combinations.html | 165 B | ||
| 15. Solving Combinations with Separate Sample Spaces.mp4 | 33.15 MB | ||
| 15. Solving Combinations with Separate Sample Spaces.srt | 3.73 KB | ||
| 16. Solving Combinations with Separate Sample Spaces.html | 165 B | ||
| 17. Combinatorics in Real-Life The Lottery.mp4 | 41.29 MB | ||
| 17. Combinatorics in Real-Life The Lottery.srt | 4.15 KB | ||
| 18. Combinatorics in Real-Life The Lottery.html | 165 B | ||
| 19. A Recap of Combinatorics.mp4 | 38.49 MB | ||
| 19. A Recap of Combinatorics.srt | 3.72 KB | ||
| 2. Fundamentals of Combinatorics.html | 165 B | ||
| 20. A Practical Example of Combinatorics.mp4 | 134.31 MB | ||
| 20. A Practical Example of Combinatorics.srt | 13.96 KB | ||
| 20.1 Additional Exercises Combinatorics.pdf.pdf | 106.58 KB | ||
| 20.2 Additional Exercises Combinatorics Solutions.pdf.pdf | 245.67 KB | ||
| 3. Permutations and How to Use Them.mp4 | 42.72 MB | ||
| 3. Permutations and How to Use Them.srt | 4.07 KB | ||
| 4. Permutations and How to Use Them.html | 165 B | ||
| 5. Simple Operations with Factorials.mp4 | 36.12 MB | ||
| 5. Simple Operations with Factorials.srt | 3.26 KB | ||
| 6. Simple Operations with Factorials.html | 165 B | ||
| 7. Solving Variations with Repetition.mp4 | 34 MB | ||
| 7. Solving Variations with Repetition.srt | 3.47 KB | ||
| 8. Solving Variations with Repetition.html | 165 B | ||
| 9. Solving Variations without Repetition.mp4 | 43.14 MB | ||
| 9. Solving Variations without Repetition.srt | 43.15 MB | ||
| 11. Probability - Bayesian Inference | |||
| 1. Sets and Events.mp4 | 53.47 MB | ||
| 1. Sets and Events.srt | 5.06 KB | ||
| 1.1 Course Notes - Bayesian Inference.pdf.pdf | 386.01 KB | ||
| 10. Mutually Exclusive Sets.html | 165 B | ||
| 11. Dependence and Independence of Sets.mp4 | 34.79 MB | ||
| 11. Dependence and Independence of Sets.srt | 3.46 KB | ||
| 12. Dependence and Independence of Sets.html | 165 B | ||
| 13. The Conditional Probability Formula.mp4 | 45.87 MB | ||
| 13. The Conditional Probability Formula.srt | 4.93 KB | ||
| 14. The Conditional Probability Formula.html | 165 B | ||
| 15. The Law of Total Probability.mp4 | 34.93 MB | ||
| 15. The Law of Total Probability.srt | 3.49 KB | ||
| 16. The Additive Rule.mp4 | 26.97 MB | ||
| 16. The Additive Rule.srt | 2.74 KB | ||
| 17. The Additive Rule.html | 165 B | ||
| 18. The Multiplication Law.mp4 | 49.02 MB | ||
| 18. The Multiplication Law.srt | 4.62 KB | ||
| 19. The Multiplication Law.html | 165 B | ||
| 2. Sets and Events.html | 165 B | ||
| 20. Bayes' Law.mp4 | 49.93 MB | ||
| 20. Bayes' Law.srt | 7.2 KB | ||
| 21. Bayes' Law.html | 165 B | ||
| 22. A Practical Example of Bayesian Inference.mp4 | 145.13 MB | ||
| 22. A Practical Example of Bayesian Inference.srt | 19.32 KB | ||
| 22.1 CDS_2017-2018 Hamilton.pdf.pdf | 845.31 KB | ||
| 22.2 Bayesian Homework - Solutions.pdf.pdf | 30.35 KB | ||
| 22.3 Bayesian Homework .pdf.pdf | 27.26 KB | ||
| 3. Ways Sets Can Interact.mp4 | 47.43 MB | ||
| 3. Ways Sets Can Interact.srt | 4.39 KB | ||
| 4. Ways Sets Can Interact.html | 165 B | ||
| 5. Intersection of Sets.mp4 | 26.96 MB | ||
| 5. Intersection of Sets.srt | 2.47 KB | ||
| 6. Intersection of Sets.html | 165 B | ||
| 7. Union of Sets.mp4 | 57.19 MB | ||
| 7. Union of Sets.srt | 5.53 KB | ||
| 8. Union of Sets.html | 165 B | ||
| 9. Mutually Exclusive Sets.mp4 | 25.39 MB | ||
| 9. Mutually Exclusive Sets.srt | 2.52 KB | ||
| 12. Probability - Distributions | |||
| 1. Fundamentals of Probability Distributions.mp4 | 73.4 MB | ||
| 1. Fundamentals of Probability Distributions.srt | 7.54 KB | ||
| 1.1 Course Notes - Probability Distributions.pdf.pdf | 463.95 KB | ||
| 10. Discrete Distributions The Bernoulli Distribution.html | 165 B | ||
| 11. Discrete Distributions The Binomial Distribution.mp4 | 68.83 MB | ||
| 11. Discrete Distributions The Binomial Distribution.srt | 8.3 KB | ||
| 12. Discrete Distributions The Binomial Distribution.html | 165 B | ||
| 13. Discrete Distributions The Poisson Distribution.mp4 | 55.76 MB | ||
| 13. Discrete Distributions The Poisson Distribution.srt | 6.57 KB | ||
| 13.1 Poisson - Expected Value and Variance.pdf.pdf | 145.99 KB | ||
| 14. Discrete Distributions The Poisson Distribution.html | 165 B | ||
| 15. Characteristics of Continuous Distributions.mp4 | 84.12 MB | ||
| 15. Characteristics of Continuous Distributions.srt | 8.66 KB | ||
| 15.1 Solving Integrals.pdf.pdf | 343.85 KB | ||
| 16. Characteristics of Continuous Distributions.html | 165 B | ||
| 17. Continuous Distributions The Normal Distribution.mp4 | 48.24 MB | ||
| 17. Continuous Distributions The Normal Distribution.srt | 4.77 KB | ||
| 17.1 Normal Distribution - Exp and Var.pdf.pdf | 144.08 KB | ||
| 18. Continuous Distributions The Normal Distribution.html | 165 B | ||
| 19. Continuous Distributions The Standard Normal Distribution.mp4 | 47.9 MB | ||
| 19. Continuous Distributions The Standard Normal Distribution.srt | 5.28 KB | ||
| 2. Fundamentals of Probability Distributions.html | 165 B | ||
| 20. Continuous Distributions The Standard Normal Distribution.html | 165 B | ||
| 21. Continuous Distributions The Students' T Distribution.mp4 | 27.18 MB | ||
| 21. Continuous Distributions The Students' T Distribution.srt | 2.79 KB | ||
| 22. Continuous Distributions The Students' T Distribution.html | 165 B | ||
| 23. Continuous Distributions The Chi-Squared Distribution.mp4 | 26.34 MB | ||
| 23. Continuous Distributions The Chi-Squared Distribution.srt | 2.76 KB | ||
| 24. Continuous Distributions The Chi-Squared Distribution.html | 165 B | ||
| 25. Continuous Distributions The Exponential Distribution.mp4 | 40.23 MB | ||
| 25. Continuous Distributions The Exponential Distribution.srt | 4.13 KB | ||
| 26. Continuous Distributions The Exponential Distribution.html | 165 B | ||
| 27. Continuous Distributions The Logistic Distribution.mp4 | 47.05 MB | ||
| 27. Continuous Distributions The Logistic Distribution.srt | 5.02 KB | ||
| 28. Continuous Distributions The Logistic Distribution.html | 165 B | ||
| 29. A Practical Example of Probability Distributions.mp4 | 157.82 MB | ||
| 29. A Practical Example of Probability Distributions.srt | 19.91 KB | ||
| 29.1 FIFA19.csv.csv | 8.65 MB | ||
| 29.2 Customers_Membership (post).xlsx.xlsx | 15.62 KB | ||
| 29.3 FIFA19 (post).csv.csv | 8.64 MB | ||
| 29.4 Daily Views.xlsx.xlsx | 9.53 KB | ||
| 29.5 Customers_Membership.xlsx.xlsx | 9.69 KB | ||
| 29.6 Daily Views (post).xlsx.xlsx | 20.21 KB | ||
| 3. Types of Probability Distributions.mp4 | 91.58 MB | ||
| 3. Types of Probability Distributions.srt | 9.45 KB | ||
| 4. Types of Probability Distributions.html | 165 B | ||
| 5. Characteristics of Discrete Distributions.mp4 | 22.7 MB | ||
| 5. Characteristics of Discrete Distributions.srt | 2.46 KB | ||
| 6. Characteristics of Discrete Distributions.html | 165 B | ||
| 7. Discrete Distributions The Uniform Distribution.mp4 | 24.39 MB | ||
| 7. Discrete Distributions The Uniform Distribution.srt | 2.73 KB | ||
| 8. Discrete Distributions The Uniform Distribution.html | 165 B | ||
| 9. Discrete Distributions The Bernoulli Distribution.mp4 | 34.13 MB | ||
| 9. Discrete Distributions The Bernoulli Distribution.srt | 3.85 KB | ||
| 13. Probability - Probability in Other Fields | |||
| 1. Probability in Finance.mp4 | 99.07 MB | ||
| 1. Probability in Finance.srt | 9.83 KB | ||
| 1.1 Probability in Finance Solutions.pdf.pdf | 184.46 KB | ||
| 1.2 Probability in Finance Homework.pdf.pdf | 110.68 KB | ||
| 2. Probability in Statistics.mp4 | 77.28 MB | ||
| 2. Probability in Statistics.srt | 8.44 KB | ||
| 3. Probability in Data Science.mp4 | 63.49 MB | ||
| 3. Probability in Data Science.srt | 6.65 KB | ||
| 14. Part 3 Statistics | |||
| 1. Population and Sample.mp4 | 58.11 MB | ||
| 1. Population and Sample.srt | 5.47 KB | ||
| 1.1 Statistics Glossary.xlsx.xlsx | 20.26 KB | ||
| 1.2 Course notes_descriptive_statistics.pdf.pdf | 482.21 KB | ||
| 2. Population and Sample.html | 165 B | ||
| 15. Statistics - Descriptive Statistics | |||
| 1. Types of Data.mp4 | 72.52 MB | ||
| 1. Types of Data.srt | 5.95 KB | ||
| 1.1 Course notes_descriptive_statistics.pdf.pdf | 482.21 KB | ||
| 1.2 Glossary.xlsx.xlsx | 19.97 KB | ||
| 10. Numerical Variables Exercise.html | 81 B | ||
| 10.1 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx | 11.75 KB | ||
| 10.2 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx | 13.15 KB | ||
| 11. The Histogram.mp4 | 13.78 MB | ||
| 11. The Histogram.srt | 3.01 KB | ||
| 11.1 2.5. The Histogram_lesson.xlsx.xlsx | 18.63 KB | ||
| 12. The Histogram.html | 165 B | ||
| 13. Histogram Exercise.html | 81 B | ||
| 13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx | 17.1 KB | ||
| 13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf | 289.12 KB | ||
| 13.3 2.5.The-Histogram-exercise.xlsx.xlsx | 15.5 KB | ||
| 14. Cross Tables and Scatter Plots.mp4 | 39.81 MB | ||
| 14. Cross Tables and Scatter Plots.srt | 6.68 KB | ||
| 14.1 2.6. Cross table and scatter plot.xlsx.xlsx | 26.12 KB | ||
| 15. Cross Tables and Scatter Plots.html | 165 B | ||
| 16. Cross Tables and Scatter Plots Exercise.html | 81 B | ||
| 16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx | 16.28 KB | ||
| 16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx | 40.44 KB | ||
| 17. Mean, median and mode.mp4 | 37.13 MB | ||
| 17. Mean, median and mode.srt | 5.73 KB | ||
| 17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx | 10.49 KB | ||
| 18. Mean, Median and Mode Exercise.html | 81 B | ||
| 18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx | 10.87 KB | ||
| 18.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx | 11.35 KB | ||
| 19. Skewness.mp4 | 19.4 MB | ||
| 19. Skewness.srt | 3.64 KB | ||
| 19.1 2.8. Skewness_lesson.xlsx.xlsx | 34.63 KB | ||
| 2. Types of Data.html | 165 B | ||
| 20. Skewness.html | 165 B | ||
| 21. Skewness Exercise.html | 81 B | ||
| 21.1 2.8. Skewness_exercise.xlsx.xlsx | 9.49 KB | ||
| 21.2 2.8. Skewness_exercise_solution.xlsx.xlsx | 19.78 KB | ||
| 22. Variance.mp4 | 50.95 MB | ||
| 22. Variance.srt | 7.53 KB | ||
| 22.1 2.9. Variance_lesson.xlsx.xlsx | 10.08 KB | ||
| 23. Variance Exercise.html | 522 B | ||
| 23.1 2.9. Variance_exercise.xlsx.xlsx | 10.83 KB | ||
| 23.2 2.9. Variance_exercise_solution.xlsx.xlsx | 11.05 KB | ||
| 24. Standard Deviation and Coefficient of Variation.mp4 | 45.12 MB | ||
| 24. Standard Deviation and Coefficient of Variation.srt | 6.6 KB | ||
| 24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx | 10.97 KB | ||
| 25. Standard Deviation.html | 165 B | ||
| 26. Standard Deviation and Coefficient of Variation Exercise.html | 81 B | ||
| 26.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx.xlsx | 12.6 KB | ||
| 26.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx.xlsx | 11.61 KB | ||
| 27. Covariance.mp4 | 27.48 MB | ||
| 27. Covariance.srt | 4.92 KB | ||
| 27.1 2.11. Covariance_lesson.xlsx.xlsx | 24.92 KB | ||
| 28. Covariance.html | 165 B | ||
| 29. Covariance Exercise.html | 81 B | ||
| 29.1 2.11. Covariance_exercise_solution.xlsx.xlsx | 29.51 KB | ||
| 29.2 2.11. Covariance_exercise.xlsx.xlsx | 20.23 KB | ||
| 3. Levels of Measurement.mp4 | 54.38 MB | ||
| 3. Levels of Measurement.srt | 4.54 KB | ||
| 30. Correlation Coefficient.mp4 | 29.38 MB | ||
| 30. Correlation Coefficient.srt | 4.71 KB | ||
| 31. Correlation.html | 165 B | ||
| 32. Correlation Coefficient Exercise.html | 81 B | ||
| 32.1 2.12. Correlation_exercise_solution.xlsx.xlsx | 29.48 KB | ||
| 32.2 2.12. Correlation_exercise.xlsx.xlsx | 29.3 KB | ||
| 4. Levels of Measurement.html | 165 B | ||
| 5. Categorical Variables - Visualization Techniques.mp4 | 38.46 MB | ||
| 5. Categorical Variables - Visualization Techniques.srt | 6.43 KB | ||
| 5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx | 30.77 KB | ||
| 6. Categorical Variables - Visualization Techniques.html | 165 B | ||
| 7. Categorical Variables Exercise.html | 81 B | ||
| 7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx | 15.24 KB | ||
| 7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf | 289.12 KB | ||
| 7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx | 41.11 KB | ||
| 8. Numerical Variables - Frequency Distribution Table.mp4 | 25.85 MB | ||
| 8. Numerical Variables - Frequency Distribution Table.srt | 4.36 KB | ||
| 8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx | 11.44 KB | ||
| 9. Numerical Variables - Frequency Distribution Table.html | 165 B | ||
| 16. Statistics - Practical Example Descriptive Statistics | |||
| 1. Practical Example Descriptive Statistics.mp4 | 160.46 MB | ||
| 1. Practical Example Descriptive Statistics.srt | 20.78 KB | ||
| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx | 146.51 KB | ||
| 2. Practical Example Descriptive Statistics Exercise.html | 81 B | ||
| 2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx | 146.38 KB | ||
| 2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx | 120.27 KB | ||
| 17. Statistics - Inferential Statistics Fundamentals | |||
| 1. Introduction.mp4 | 15.5 MB | ||
| 1. Introduction.srt | 1.63 KB | ||
| 1.1 Course notes_inferential statistics.pdf.pdf | 382.32 KB | ||
| 10. Central Limit Theorem.html | 165 B | ||
| 11. Standard error.mp4 | 22.78 MB | ||
| 11. Standard error.srt | 2.02 KB | ||
| 12. Standard Error.html | 165 B | ||
| 13. Estimators and Estimates.mp4 | 47.83 MB | ||
| 13. Estimators and Estimates.srt | 3.71 KB | ||
| 14. Estimators and Estimates.html | 165 B | ||
| 2. What is a Distribution.mp4 | 61.59 MB | ||
| 2. What is a Distribution.srt | 5.85 KB | ||
| 2.1 3.2. What is a distribution_lesson.xlsx.xlsx | 19.46 KB | ||
| 2.2 Course notes_inferential statistics.pdf.pdf | 382.32 KB | ||
| 3. What is a Distribution.html | 165 B | ||
| 4. The Normal Distribution.mp4 | 49.85 MB | ||
| 4. The Normal Distribution.srt | 4.9 KB | ||
| 5. The Normal Distribution.html | 165 B | ||
| 6. The Standard Normal Distribution.mp4 | 22.51 MB | ||
| 6. The Standard Normal Distribution.srt | 3.94 KB | ||
| 6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx | 10.38 KB | ||
| 7. The Standard Normal Distribution.html | 165 B | ||
| 8. The Standard Normal Distribution Exercise.html | 81 B | ||
| 8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx | 11.99 KB | ||
| 8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx | 24.04 KB | ||
| 9. Central Limit Theorem.mp4 | 62.89 MB | ||
| 9. Central Limit Theorem.srt | 5.63 KB | ||
| 18. Statistics - Inferential Statistics Confidence Intervals | |||
| 1. What are Confidence Intervals.mp4 | 49.99 MB | ||
| 1. What are Confidence Intervals.srt | 3.26 KB | ||
| 10. Margin of Error.mp4 | 59.17 MB | ||
| 10. Margin of Error.srt | 6.11 KB | ||
| 11. Margin of Error.html | 165 B | ||
| 12. Confidence intervals. Two means. Dependent samples.mp4 | 70.48 MB | ||
| 12. Confidence intervals. Two means. Dependent samples.srt | 32 MB | ||
| 12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx | 10.47 KB | ||
| 13. Confidence intervals. Two means. Dependent samples Exercise.html | 81 B | ||
| 13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx | 13.74 KB | ||
| 13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx | 14.24 KB | ||
| 14. Confidence intervals. Two means. Independent samples (Part 1).mp4 | 28.76 MB | ||
| 14. Confidence intervals. Two means. Independent samples (Part 1).srt | 6.07 KB | ||
| 14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx | 9.83 KB | ||
| 15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html | 81 B | ||
| 15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx | 10.12 KB | ||
| 15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx | 9.83 KB | ||
| 16. Confidence intervals. Two means. Independent samples (Part 2).mp4 | 26.83 MB | ||
| 16. Confidence intervals. Two means. Independent samples (Part 2).srt | 4.51 KB | ||
| 16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx | 9.52 KB | ||
| 17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html | 81 B | ||
| 17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx | 9.17 KB | ||
| 17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx | 9.79 KB | ||
| 18. Confidence intervals. Two means. Independent samples (Part 3).mp4 | 19.94 MB | ||
| 18. Confidence intervals. Two means. Independent samples (Part 3).srt | 1.96 KB | ||
| 2. What are Confidence Intervals.html | 165 B | ||
| 3. Confidence Intervals; Population Variance Known; z-score.mp4 | 78.21 MB | ||
| 3. Confidence Intervals; Population Variance Known; z-score.srt | 9.8 KB | ||
| 3.1 3.9. Population variance known, z-score_lesson.xlsx.xlsx | 11.21 KB | ||
| 3.2 3.9.The-z-table.xlsx.xlsx | 25.58 KB | ||
| 4. Confidence Intervals; Population Variance Known; z-score; Exercise.html | 81 B | ||
| 4.1 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx | 11.16 KB | ||
| 4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx | 10.83 KB | ||
| 4.3 3.9.The-z-table.xlsx.xlsx | 25.58 KB | ||
| 5. Confidence Interval Clarifications.mp4 | 57.03 MB | ||
| 5. Confidence Interval Clarifications.srt | 5.41 KB | ||
| 6. Student's T Distribution.mp4 | 35.44 MB | ||
| 6. Student's T Distribution.srt | 4.13 KB | ||
| 7. Student's T Distribution.html | 165 B | ||
| 8. Confidence Intervals; Population Variance Unknown; t-score.mp4 | 32.21 MB | ||
| 8. Confidence Intervals; Population Variance Unknown; t-score.srt | 5.71 KB | ||
| 8.1 3.11. The t-table.xlsx.xlsx | 15.85 KB | ||
| 8.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx | 10.78 KB | ||
| 9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html | 81 B | ||
| 9.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx | 11.1 KB | ||
| 9.2 3.11.The-t-table.xlsx.xlsx | 15.85 KB | ||
| 9.3 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx | 10.62 KB | ||
| 19. Statistics - Practical Example Inferential Statistics | |||
| 1. Practical Example Inferential Statistics.mp4 | 102.67 MB | ||
| 1. Practical Example Inferential Statistics.srt | 13.64 KB | ||
| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx | 1.74 MB | ||
| 2. Practical Example Inferential Statistics Exercise.html | 81 B | ||
| 2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx.xlsx | 1.73 MB | ||
| 2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx | 1.82 MB | ||
| 2. The Field of Data Science - The Various Data Science Disciplines | |||
| 1. Data Science and Business Buzzwords Why are there so many.mp4 | 81.41 MB | ||
| 1. Data Science and Business Buzzwords Why are there so many.srt | 6.62 KB | ||
| 10. A Breakdown of our Data Science Infographic.html | 165 B | ||
| 2. Data Science and Business Buzzwords Why are there so many.html | 165 B | ||
| 3. What is the difference between Analysis and Analytics.mp4 | 53.55 MB | ||
| 3. What is the difference between Analysis and Analytics.srt | 5.07 KB | ||
| 4. What is the difference between Analysis and Analytics.html | 165 B | ||
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 | 64.51 MB | ||
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.srt | 10.63 KB | ||
| 5.1 365_DataScience_Diagram.pdf.pdf | 323.08 KB | ||
| 6. Business Analytics, Data Analytics, and Data Science An Introduction.html | 165 B | ||
| 7. Continuing with BI, ML, and AI.mp4 | 108.99 MB | ||
| 7. Continuing with BI, ML, and AI.srt | 11.87 KB | ||
| 7.1 365_DataScience_Diagram.pdf.pdf | 323.08 KB | ||
| 7.2 365_DataScience.png.png | 6.93 MB | ||
| 8. Continuing with BI, ML, and AI.html | 165 B | ||
| 9. A Breakdown of our Data Science Infographic.mp4 | 67.75 MB | ||
| 9. A Breakdown of our Data Science Infographic.srt | 5.1 KB | ||
| 9.1 365_DataScience.png.png | 6.93 MB | ||
| 20. Statistics - Hypothesis Testing | |||
| 1. Null vs Alternative Hypothesis.mp4 | 92.05 MB | ||
| 1. Null vs Alternative Hypothesis.srt | 6.97 KB | ||
| 1.1 Course notes_hypothesis_testing.pdf.pdf | 648.2 KB | ||
| 10. p-value.mp4 | 55.87 MB | ||
| 10. p-value.srt | 55.88 MB | ||
| 10.1 Online p-value calculator.pdf.pdf | 1.15 MB | ||
| 11. p-value.html | 165 B | ||
| 12. Test for the Mean. Population Variance Unknown.mp4 | 40.25 MB | ||
| 12. Test for the Mean. Population Variance Unknown.srt | 5.73 KB | ||
| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx | 14.54 KB | ||
| 13. Test for the Mean. Population Variance Unknown Exercise.html | 81 B | ||
| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx | 12.63 KB | ||
| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx | 11.34 KB | ||
| 14. Test for the Mean. Dependent Samples.mp4 | 50.37 MB | ||
| 14. Test for the Mean. Dependent Samples.srt | 6.26 KB | ||
| 14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx | 9.79 KB | ||
| 15. Test for the Mean. Dependent Samples Exercise.html | 81 B | ||
| 15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx | 14.4 KB | ||
| 15.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx | 12.8 KB | ||
| 16. Test for the mean. Independent samples (Part 1).mp4 | 33.94 MB | ||
| 16. Test for the mean. Independent samples (Part 1).srt | 5.49 KB | ||
| 16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx | 9.63 KB | ||
| 17. Test for the mean. Independent samples (Part 1). Exercise.html | 81 B | ||
| 17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx | 10.77 KB | ||
| 17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx | 11.25 KB | ||
| 18. Test for the mean. Independent samples (Part 2).mp4 | 36.39 MB | ||
| 18. Test for the mean. Independent samples (Part 2).srt | 5.14 KB | ||
| 18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx | 9.31 KB | ||
| 19. Test for the mean. Independent samples (Part 2).html | 165 B | ||
| 2. Further Reading on Null and Alternative Hypothesis.html | 2.29 KB | ||
| 20. Test for the mean. Independent samples (Part 2) Exercise.html | 81 B | ||
| 20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx | 10.54 KB | ||
| 20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx | 11.39 KB | ||
| 3. Null vs Alternative Hypothesis.html | 165 B | ||
| 4. Rejection Region and Significance Level.mp4 | 82.62 MB | ||
| 4. Rejection Region and Significance Level.srt | 8.69 KB | ||
| 4.1 Course notes_hypothesis_testing.pdf.pdf | 648.2 KB | ||
| 5. Rejection Region and Significance Level.html | 165 B | ||
| 6. Type I Error and Type II Error.mp4 | 43.93 MB | ||
| 6. Type I Error and Type II Error.srt | 5.67 KB | ||
| 7. Type I Error and Type II Error.html | 165 B | ||
| 8. Test for the Mean. Population Variance Known.mp4 | 54.22 MB | ||
| 8. Test for the Mean. Population Variance Known.srt | 8.14 KB | ||
| 8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx | 10.96 KB | ||
| 9. Test for the Mean. Population Variance Known Exercise.html | 81 B | ||
| 9.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx | 11.22 KB | ||
| 9.2 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx | 11.03 KB | ||
| 21. Statistics - Practical Example Hypothesis Testing | |||
| 1. Practical Example Hypothesis Testing.mp4 | 69.49 MB | ||
| 1. Practical Example Hypothesis Testing.srt | 8.49 KB | ||
| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx | 51.71 KB | ||
| 2. Practical Example Hypothesis Testing Exercise.html | 81 B | ||
| 2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx | 44.04 KB | ||
| 2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx | 43.38 KB | ||
| 22. Part 4 Introduction to Python | |||
| 1. Introduction to Programming.mp4 | 58.55 MB | ||
| 1. Introduction to Programming.srt | 6.9 KB | ||
| 10. Jupyter's Interface.html | 165 B | ||
| 11. Python 2 vs Python 3.mp4 | 11.27 MB | ||
| 11. Python 2 vs Python 3.srt | 3.31 KB | ||
| 11.1 Python Introduction - Course Notes.pdf.pdf | 2.03 MB | ||
| 2. Introduction to Programming.html | 165 B | ||
| 3. Why Python.mp4 | 75.08 MB | ||
| 3. Why Python.srt | 75.09 MB | ||
| 4. Why Python.html | 165 B | ||
| 5. Why Jupyter.mp4 | 44.31 MB | ||
| 5. Why Jupyter.srt | 88.63 MB | ||
| 6. Why Jupyter.html | 165 B | ||
| 7. Installing Python and Jupyter.mp4 | 50.99 MB | ||
| 7. Installing Python and Jupyter.srt | 8.84 KB | ||
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 | 13.79 MB | ||
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.srt | 3.73 KB | ||
| 9. Prerequisites for Coding in the Jupyter Notebooks.mp4 | 30.58 MB | ||
| 9. Prerequisites for Coding in the Jupyter Notebooks.srt | 7.79 KB | ||
| 23. Python - Variables and Data Types | |||
| 1. Variables.mp4 | 25.3 MB | ||
| 1. Variables.srt | 6.05 KB | ||
| 1.1 Python Introduction - Course Notes.pdf.pdf | 2.03 MB | ||
| 1.2 Variables - Resources.html | 134 B | ||
| 2. Variables.html | 165 B | ||
| 3. Numbers and Boolean Values in Python.mp4 | 17.06 MB | ||
| 3. Numbers and Boolean Values in Python.srt | 3.68 KB | ||
| 3.1 Numbers and Boolean Values - Resources.html | 134 B | ||
| 4. Numbers and Boolean Values in Python.html | 165 B | ||
| 5. Python Strings.mp4 | 50.64 MB | ||
| 5. Python Strings.srt | 14.55 KB | ||
| 5.1 Strings - Resources.html | 134 B | ||
| 6. Python Strings.html | 165 B | ||
| 24. Python - Basic Python Syntax | |||
| 1. Using Arithmetic Operators in Python.mp4 | 18.92 MB | ||
| 1. Using Arithmetic Operators in Python.srt | 4.11 KB | ||
| 1.1 Arithmetic Operators - Resources.html | 134 B | ||
| 10. Indexing Elements.mp4 | 5.93 MB | ||
| 10. Indexing Elements.srt | 1.7 KB | ||
| 10.1 Indexing Elements - Resources.html | 134 B | ||
| 11. Indexing Elements.html | 165 B | ||
| 12. Structuring with Indentation.mp4 | 13.15 MB | ||
| 12. Structuring with Indentation.srt | 4.68 KB | ||
| 12.1 Structure Your Code with Indentation - Resources.html | 134 B | ||
| 13. Structuring with Indentation.html | 165 B | ||
| 2. Using Arithmetic Operators in Python.html | 165 B | ||
| 3. The Double Equality Sign.mp4 | 5.99 MB | ||
| 3. The Double Equality Sign.srt | 1.82 KB | ||
| 3.1 The Double Equality Sign - Resources.html | 134 B | ||
| 4. The Double Equality Sign.html | 165 B | ||
| 5. How to Reassign Values.mp4 | 4.01 MB | ||
| 5. How to Reassign Values.srt | 1.29 KB | ||
| 5.1 Reassign Values - Resources.html | 134 B | ||
| 6. How to Reassign Values.html | 165 B | ||
| 7. Add Comments.mp4 | 11.26 MB | ||
| 7. Add Comments.srt | 3.87 KB | ||
| 7.1 Add Comments - Resources.html | 134 B | ||
| 8. Add Comments.html | 165 B | ||
| 9. Understanding Line Continuation.mp4 | 2.35 MB | ||
| 9. Understanding Line Continuation.srt | 1.13 KB | ||
| 9.1 Line Continuation - Resources.html | 134 B | ||
| 25. Python - Other Python Operators | |||
| 1. Comparison Operators.mp4 | 10.17 MB | ||
| 1. Comparison Operators.srt | 2.47 KB | ||
| 1.1 Comparison Operators - Resources.html | 134 B | ||
| 2. Comparison Operators.html | 165 B | ||
| 3. Logical and Identity Operators.mp4 | 30.05 MB | ||
| 3. Logical and Identity Operators.srt | 5.77 KB | ||
| 3.1 Logical and Identity Operators - Resources.html | 134 B | ||
| 4. Logical and Identity Operators.html | 165 B | ||
| 26. Python - Conditional Statements | |||
| 1. The IF Statement.mp4 | 23.24 MB | ||
| 1. The IF Statement.srt | 7.6 KB | ||
| 1.1 Introduction to the If Statement - Resources.html | 134 B | ||
| 2. The IF Statement.html | 165 B | ||
| 3. The ELSE Statement.mp4 | 23.28 MB | ||
| 3. The ELSE Statement.srt | 6.28 KB | ||
| 3.1 Add an Else Statement - Resources.html | 134 B | ||
| 4. The ELIF Statement.mp4 | 53.34 MB | ||
| 4. The ELIF Statement.srt | 53.35 MB | ||
| 4.1 Else if, for Brief - Elif - Resources.html | 134 B | ||
| 5. A Note on Boolean Values.mp4 | 19.99 MB | ||
| 5. A Note on Boolean Values.srt | 6.21 KB | ||
| 5.1 A Note on Boolean Values - Resources.html | 134 B | ||
| 6. A Note on Boolean Values.html | 165 B | ||
| 27. Python - Python Functions | |||
| 1. Defining a Function in Python.mp4 | 14.75 MB | ||
| 1. Defining a Function in Python.srt | 5.32 KB | ||
| 1.1 Defining a Function in Python - Resources.html | 134 B | ||
| 2. How to Create a Function with a Parameter.mp4 | 38.1 MB | ||
| 2. How to Create a Function with a Parameter.srt | 8.98 KB | ||
| 2.1 Creating a Function with a Parameter - Resources.html | 134 B | ||
| 3. Defining a Function in Python - Part II.mp4 | 25.24 MB | ||
| 3. Defining a Function in Python - Part II.srt | 25.25 MB | ||
| 3.1 Another Way to Define a Function - Resources.html | 134 B | ||
| 4. How to Use a Function within a Function.mp4 | 8.14 MB | ||
| 4. How to Use a Function within a Function.srt | 2.03 KB | ||
| 4.1 Using a Function in Another Function - Resources.html | 134 B | ||
| 5. Conditional Statements and Functions.mp4 | 15.68 MB | ||
| 5. Conditional Statements and Functions.srt | 3.51 KB | ||
| 5.1 Combining Conditional Statements and Functions - Resources.html | 134 B | ||
| 6. Functions Containing a Few Arguments.mp4 | 14.72 MB | ||
| 6. Functions Containing a Few Arguments.srt | 3 KB | ||
| 6.1 Creating Functions Containing a Few Arguments - Resources.html | 134 B | ||
| 7. Built-in Functions in Python.mp4 | 22.02 MB | ||
| 7. Built-in Functions in Python.srt | 4.21 KB | ||
| 7.1 Notable Built-In Functions in Python - Resources.html | 134 B | ||
| 8. Python Functions.html | 165 B | ||
| 28. Python - Sequences | |||
| 1. Lists.mp4 | 37.8 MB | ||
| 1. Lists.srt | 9.83 KB | ||
| 1.1 Lists - Resources.html | 134 B | ||
| 2. Lists.html | 165 B | ||
| 3. Using Methods.mp4 | 37.59 MB | ||
| 3. Using Methods.srt | 8.36 KB | ||
| 3.1 Help Yourself with Methods - Resources.html | 134 B | ||
| 4. Using Methods.html | 165 B | ||
| 5. List Slicing.mp4 | 30.76 MB | ||
| 5. List Slicing.srt | 5.55 KB | ||
| 5.1 List Slicing - Resources.html | 134 B | ||
| 6. Tuples.mp4 | 29.5 MB | ||
| 6. Tuples.srt | 6.92 KB | ||
| 6.1 Tuples - Resources.html | 134 B | ||
| 7. Dictionaries.mp4 | 41.69 MB | ||
| 7. Dictionaries.srt | 8.43 KB | ||
| 7.1 Dictionaries - Resources.html | 134 B | ||
| 8. Dictionaries.html | 165 B | ||
| 29. Python - Iterations | |||
| 1. For Loops.mp4 | 23.6 MB | ||
| 1. For Loops.srt | 6.58 KB | ||
| 1.1 For Loops - Resources.html | 134 B | ||
| 2. For Loops.html | 165 B | ||
| 3. While Loops and Incrementing.mp4 | 28.44 MB | ||
| 3. While Loops and Incrementing.srt | 5.9 KB | ||
| 3.1 While Loops and Incrementing - Resources.html | 134 B | ||
| 4. Lists with the range() Function.mp4 | 25.79 MB | ||
| 4. Lists with the range() Function.srt | 7.66 KB | ||
| 4.1 Create Lists with the range() Function - Resources.html | 134 B | ||
| 5. Lists with the range() Function.html | 165 B | ||
| 6. Conditional Statements and Loops.mp4 | 27.76 MB | ||
| 6. Conditional Statements and Loops.srt | 7.46 KB | ||
| 6.1 Use Conditional Statements and Loops Together - Resources.html | 134 B | ||
| 7. Conditional Statements, Functions, and Loops.mp4 | 9.48 MB | ||
| 7. Conditional Statements, Functions, and Loops.srt | 2.41 KB | ||
| 7.1 All In - Conditional Statements, Functions, and Loops - Resources.html | 134 B | ||
| 8. How to Iterate over Dictionaries.mp4 | 29.66 MB | ||
| 8. How to Iterate over Dictionaries.srt | 7.93 KB | ||
| 8.1 Iterating over Dictionaries - Resources.html | 134 B | ||
| 3. The Field of Data Science - Connecting the Data Science Disciplines | |||
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 | 126.88 MB | ||
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt | 8.99 KB | ||
| 2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html | 165 B | ||
| 30. Python - Advanced Python Tools | |||
| 1. Object Oriented Programming.mp4 | 33.6 MB | ||
| 1. Object Oriented Programming.srt | 6.1 KB | ||
| 2. Object Oriented Programming.html | 165 B | ||
| 3. Modules and Packages.mp4 | 8.51 MB | ||
| 3. Modules and Packages.srt | 1.26 KB | ||
| 4. Modules and Packages.html | 165 B | ||
| 5. What is the Standard Library.mp4 | 18.03 MB | ||
| 5. What is the Standard Library.srt | 3.56 KB | ||
| 6. What is the Standard Library.html | 165 B | ||
| 7. Importing Modules in Python.mp4 | 19.93 MB | ||
| 7. Importing Modules in Python.srt | 4.81 KB | ||
| 8. Importing Modules in Python.html | 165 B | ||
| 31. Part 5 Advanced Statistical Methods in Python | |||
| 1. Introduction to Regression Analysis.mp4 | 17.33 MB | ||
| 1. Introduction to Regression Analysis.srt | 2.21 KB | ||
| 2. Introduction to Regression Analysis.html | 165 B | ||
| 32. Advanced Statistical Methods - Linear regression with StatsModels | |||
| 1. The Linear Regression Model.mp4 | 57.38 MB | ||
| 1. The Linear Regression Model.srt | 7.06 KB | ||
| 10. Using Seaborn for Graphs.mp4 | 12.24 MB | ||
| 10. Using Seaborn for Graphs.srt | 1.48 KB | ||
| 11. How to Interpret the Regression Table.mp4 | 44.65 MB | ||
| 11. How to Interpret the Regression Table.srt | 6.3 KB | ||
| 12. How to Interpret the Regression Table.html | 165 B | ||
| 13. Decomposition of Variability.mp4 | 49.66 MB | ||
| 13. Decomposition of Variability.srt | 4.17 KB | ||
| 14. Decomposition of Variability.html | 165 B | ||
| 15. What is the OLS.mp4 | 28.31 MB | ||
| 15. What is the OLS.srt | 3.82 KB | ||
| 16. What is the OLS.html | 165 B | ||
| 17. R-Squared.mp4 | 41.03 MB | ||
| 17. R-Squared.srt | 6.57 KB | ||
| 18. R-Squared.html | 165 B | ||
| 2. The Linear Regression Model.html | 165 B | ||
| 3. Correlation vs Regression.mp4 | 14.73 MB | ||
| 3. Correlation vs Regression.srt | 2.1 KB | ||
| 4. Correlation vs Regression.html | 165 B | ||
| 5. Geometrical Representation of the Linear Regression Model.mp4 | 5.13 MB | ||
| 5. Geometrical Representation of the Linear Regression Model.srt | 1.64 KB | ||
| 6. Geometrical Representation of the Linear Regression Model.html | 165 B | ||
| 7. Python Packages Installation.mp4 | 40.59 MB | ||
| 7. Python Packages Installation.srt | 19.18 MB | ||
| 8. First Regression in Python.mp4 | 44.56 MB | ||
| 8. First Regression in Python.srt | 7.91 KB | ||
| 8.1 First regression in Python.html | 134 B | ||
| 9. First Regression in Python Exercise.html | 1.33 KB | ||
| 9.1 First regression in Python - Exercise.html | 134 B | ||
| 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels | |||
| 1. Multiple Linear Regression.mp4 | 21.52 MB | ||
| 1. Multiple Linear Regression.srt | 3.35 KB | ||
| 10. A1 Linearity.html | 165 B | ||
| 11. A2 No Endogeneity.mp4 | 35.67 MB | ||
| 11. A2 No Endogeneity.srt | 5.24 KB | ||
| 12. A2 No Endogeneity.html | 165 B | ||
| 13. A3 Normality and Homoscedasticity.mp4 | 42.7 MB | ||
| 13. A3 Normality and Homoscedasticity.srt | 6.67 KB | ||
| 14. A4 No Autocorrelation.mp4 | 31.51 MB | ||
| 14. A4 No Autocorrelation.srt | 4.9 KB | ||
| 15. A4 No autocorrelation.html | 165 B | ||
| 16. A5 No Multicollinearity.mp4 | 28.71 MB | ||
| 16. A5 No Multicollinearity.srt | 4.62 KB | ||
| 17. A5 No Multicollinearity.html | 165 B | ||
| 18. Dealing with Categorical Data - Dummy Variables.mp4 | 55.67 MB | ||
| 18. Dealing with Categorical Data - Dummy Variables.srt | 8.15 KB | ||
| 18.1 Dealing with categorical data.html | 134 B | ||
| 19. Dealing with Categorical Data - Dummy Variables.html | 76 B | ||
| 19.1 Dealing with categorical data.html | 134 B | ||
| 2. Multiple Linear Regression.html | 165 B | ||
| 20. Making Predictions with the Linear Regression.mp4 | 24.69 MB | ||
| 20. Making Predictions with the Linear Regression.srt | 4.44 KB | ||
| 20.1 Making predictions.html | 134 B | ||
| 3. Adjusted R-Squared.mp4 | 54.84 MB | ||
| 3. Adjusted R-Squared.srt | 7.53 KB | ||
| 3.1 Adjusted R-squared.html | 134 B | ||
| 4. Adjusted R-Squared.html | 165 B | ||
| 5. Multiple Linear Regression Exercise.html | 76 B | ||
| 5.1 Multiple linear regression - exercise.html | 134 B | ||
| 6. Test for Significance of the Model (F-Test).mp4 | 16.43 MB | ||
| 6. Test for Significance of the Model (F-Test).srt | 2.55 KB | ||
| 7. OLS Assumptions.mp4 | 21.86 MB | ||
| 7. OLS Assumptions.srt | 3.03 KB | ||
| 8. OLS Assumptions.html | 165 B | ||
| 9. A1 Linearity.mp4 | 12.61 MB | ||
| 9. A1 Linearity.srt | 2.36 KB | ||
| 34. Advanced Statistical Methods - Linear Regression with sklearn | |||
| 1. What is sklearn and How is it Different from Other Packages.mp4 | 27.26 MB | ||
| 1. What is sklearn and How is it Different from Other Packages.srt | 3.42 KB | ||
| 10. Feature Selection (F-regression).mp4 | 29.52 MB | ||
| 10. Feature Selection (F-regression).srt | 29.52 MB | ||
| 10.1 Feature selection.html | 134 B | ||
| 11. A Note on Calculation of P-values with sklearn.html | 372 B | ||
| 11.1 Calculation of P-values.html | 134 B | ||
| 12. Creating a Summary Table with p-values.mp4 | 12.3 MB | ||
| 12. Creating a Summary Table with p-values.srt | 12.32 MB | ||
| 12.1 Summary table with p-values.html | 134 B | ||
| 13. Multiple Linear Regression - Exercise.html | 76 B | ||
| 13.1 Multiple linear regression - Exercise.html | 134 B | ||
| 14. Feature Scaling (Standardization).mp4 | 39.08 MB | ||
| 14. Feature Scaling (Standardization).srt | 7.11 MB | ||
| 14.1 Feature scaling.html | 134 B | ||
| 15. Feature Selection through Standardization of Weights.mp4 | 34.89 MB | ||
| 15. Feature Selection through Standardization of Weights.srt | 7.26 KB | ||
| 15.1 Feature scaling standardization.html | 134 B | ||
| 16. Predicting with the Standardized Coefficients.mp4 | 25.97 MB | ||
| 16. Predicting with the Standardized Coefficients.srt | 5.59 KB | ||
| 16.1 Predicting with the Standardized Cofficients.html | 134 B | ||
| 17. Feature Scaling (Standardization) - Exercise.html | 76 B | ||
| 17.1 Feature scaling - exercise.html | 134 B | ||
| 18. Underfitting and Overfitting.mp4 | 16.95 MB | ||
| 18. Underfitting and Overfitting.srt | 3.45 KB | ||
| 19. Train - Test Split Explained.mp4 | 49.17 MB | ||
| 19. Train - Test Split Explained.srt | 9.59 KB | ||
| 19.1 Train - Test split explained.html | 134 B | ||
| 2. How are Going to Approach this Section.mp4 | 19.41 MB | ||
| 2. How are Going to Approach this Section.srt | 2.92 KB | ||
| 3. Simple Linear Regression with sklearn.mp4 | 34.78 MB | ||
| 3. Simple Linear Regression with sklearn.srt | 7.33 KB | ||
| 3.1 Simple Linear Regression with sklearn with Comments.html | 170 B | ||
| 3.2 Simple Linear Regression with sklearn.html | 156 B | ||
| 3.3 1.01. Simple linear regression.csv.csv | 922 B | ||
| 4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 | 32.01 MB | ||
| 4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt | 6.69 KB | ||
| 4.1 Simple Linear Regression with sklearn.html | 156 B | ||
| 4.2 1.01. Simple linear regression.csv.csv | 922 B | ||
| 4.3 Simple Linear Regression with sklearn with Comments.html | 170 B | ||
| 5. A Note on Normalization.html | 733 B | ||
| 6. Simple Linear Regression with sklearn - Exercise.html | 76 B | ||
| 6.1 Simple linear regression with sklearn.html | 134 B | ||
| 7. Multiple Linear Regression with sklearn.mp4 | 20.07 MB | ||
| 7. Multiple Linear Regression with sklearn.srt | 4.17 KB | ||
| 7.1 Multiple Linear Regression with sklearn.html | 158 B | ||
| 7.2 1.02. Multiple linear regression.csv.csv | 1.07 KB | ||
| 7.3 Multiple Linear Regression with sklearn with Comments.html | 172 B | ||
| 8. Calculating the Adjusted R-Squared in sklearn.mp4 | 30.88 MB | ||
| 8. Calculating the Adjusted R-Squared in sklearn.srt | 6.27 KB | ||
| 8.1 Multiple Linear Regression and Adjusted R-squared with Comments.html | 201 B | ||
| 8.2 Multiple Linear Regression and Adjusted R-squared.html | 187 B | ||
| 8.3 1.02. Multiple linear regression.csv.csv | 1.07 KB | ||
| 9. Calculating the Adjusted R-Squared in sklearn - Exercise.html | 76 B | ||
| 9.1 Calculating the Adjusted R-Squared.html | 134 B | ||
| 35. Advanced Statistical Methods - Practical Example Linear Regression | |||
| 1. Practical Example Linear Regression (Part 1).mp4 | 97.08 MB | ||
| 1. Practical Example Linear Regression (Part 1).srt | 14.86 KB | ||
| 1.1 sklearn - Linear Regression - Practical Example (Part 1).html | 134 B | ||
| 2. Practical Example Linear Regression (Part 2).mp4 | 46.01 MB | ||
| 2. Practical Example Linear Regression (Part 2).srt | 8.03 KB | ||
| 2.1 sklearn - Linear Regression - Practical Example (Part 2).html | 134 B | ||
| 3. A Note on Multicollinearity.html | 849 B | ||
| 4. Practical Example Linear Regression (Part 3).mp4 | 23.69 MB | ||
| 4. Practical Example Linear Regression (Part 3).srt | 4.12 KB | ||
| 4.1 sklearn - Linear Regression - Practical Example (Part 3).html | 134 B | ||
| 5. Dummies and Variance Inflation Factor - Exercise.html | 76 B | ||
| 5.1 Dummies and VIF - Exercise and Solution.html | 134 B | ||
| 6. Practical Example Linear Regression (Part 4).mp4 | 56.05 MB | ||
| 6. Practical Example Linear Regression (Part 4).srt | 11.49 KB | ||
| 6.1 sklearn - Linear Regression - Practical Example (Part 4).html | 134 B | ||
| 7. Dummy Variables - Exercise.html | 713 B | ||
| 8. Practical Example Linear Regression (Part 5).mp4 | 57.89 MB | ||
| 8. Practical Example Linear Regression (Part 5).srt | 10.59 KB | ||
| 8.1 sklearn - Linear Regression - Practical Example (Part 5).html | 134 B | ||
| 9. Linear Regression - Exercise.html | 503 B | ||
| 36. Advanced Statistical Methods - Logistic Regression | |||
| 1. Introduction to Logistic Regression.mp4 | 27.07 MB | ||
| 1. Introduction to Logistic Regression.srt | 1.61 KB | ||
| 10. Binary Predictors in a Logistic Regression.mp4 | 38.44 MB | ||
| 10. Binary Predictors in a Logistic Regression.srt | 5.41 KB | ||
| 10.1 Binary predictors.html | 134 B | ||
| 11. Binary Predictors in a Logistic Regression - Exercise.html | 87 B | ||
| 11.1 Binary predictors - exercise.html | 134 B | ||
| 11.2 Bank_data.csv.csv | 19.55 KB | ||
| 12. Calculating the Accuracy of the Model.mp4 | 32.85 MB | ||
| 12. Calculating the Accuracy of the Model.srt | 4.13 KB | ||
| 12.1 Accuracy.html | 134 B | ||
| 13. Calculating the Accuracy of the Model.html | 87 B | ||
| 13.1 Bank_data.csv.csv | 19.55 KB | ||
| 13.2 Accuracy of the model - exercise.html | 134 B | ||
| 14. Underfitting and Overfitting.mp4 | 22.3 MB | ||
| 14. Underfitting and Overfitting.srt | 4.97 KB | ||
| 15. Testing the Model.mp4 | 32.28 MB | ||
| 15. Testing the Model.srt | 6.55 KB | ||
| 15.1 Testing the model.html | 134 B | ||
| 16. Testing the Model - Exercise.html | 87 B | ||
| 16.1 Testing the model - exercise.html | 134 B | ||
| 16.2 Bank_data.csv.csv | 19.55 KB | ||
| 16.3 Bank_data_testing.csv.csv | 8.3 KB | ||
| 2. A Simple Example in Python.mp4 | 34.69 MB | ||
| 2. A Simple Example in Python.srt | 5.79 KB | ||
| 2.1 A simple example in Python.html | 134 B | ||
| 3. Logistic vs Logit Function.mp4 | 86.49 MB | ||
| 3. Logistic vs Logit Function.srt | 4.88 KB | ||
| 4. Building a Logistic Regression.mp4 | 17.11 MB | ||
| 4. Building a Logistic Regression.srt | 3.28 KB | ||
| 4.1 Building a logistic regression.html | 134 B | ||
| 5. Building a Logistic Regression - Exercise.html | 87 B | ||
| 5.1 Example_bank_data.csv.csv | 6.21 KB | ||
| 5.2 Building a logistic regression.html | 134 B | ||
| 6. An Invaluable Coding Tip.mp4 | 23.05 MB | ||
| 6. An Invaluable Coding Tip.srt | 3.2 KB | ||
| 7. Understanding Logistic Regression Tables.mp4 | 30.56 MB | ||
| 7. Understanding Logistic Regression Tables.srt | 5.56 KB | ||
| 8. Understanding Logistic Regression Tables - Exercise.html | 87 B | ||
| 8.1 Understanding logistic regression.html | 134 B | ||
| 8.2 Bank_data.csv.csv | 19.55 KB | ||
| 9. What do the Odds Actually Mean.mp4 | 32.28 MB | ||
| 9. What do the Odds Actually Mean.srt | 4.79 KB | ||
| 37. Advanced Statistical Methods - Cluster Analysis | |||
| 1. Introduction to Cluster Analysis.mp4 | 53.42 MB | ||
| 1. Introduction to Cluster Analysis.srt | 4.8 KB | ||
| 2. Some Examples of Clusters.mp4 | 71.53 MB | ||
| 2. Some Examples of Clusters.srt | 6.24 KB | ||
| 3. Difference between Classification and Clustering.mp4 | 36.16 MB | ||
| 3. Difference between Classification and Clustering.srt | 3.28 KB | ||
| 4. Math Prerequisites.mp4 | 14.56 MB | ||
| 4. Math Prerequisites.srt | 4.05 KB | ||
| 38. Advanced Statistical Methods - K-Means Clustering | |||
| 1. K-Means Clustering.mp4 | 27.29 MB | ||
| 1. K-Means Clustering.srt | 6.67 KB | ||
| 10. Relationship between Clustering and Regression.mp4 | 9.93 MB | ||
| 10. Relationship between Clustering and Regression.srt | 2.18 KB | ||
| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 | 43.01 MB | ||
| 11. Market Segmentation with Cluster Analysis (Part 1).srt | 7.53 KB | ||
| 11.1 Market segmentation.html | 134 B | ||
| 12. Market Segmentation with Cluster Analysis (Part 2).mp4 | 56.12 MB | ||
| 12. Market Segmentation with Cluster Analysis (Part 2).srt | 9.18 KB | ||
| 12.1 Market segmentation.html | 134 B | ||
| 13. How is Clustering Useful.mp4 | 74.45 MB | ||
| 13. How is Clustering Useful.srt | 6.39 KB | ||
| 14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html | 87 B | ||
| 14.1 iris_dataset.csv.csv | 2.4 KB | ||
| 14.2 Exercise - part 1.html | 134 B | ||
| 15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html | 87 B | ||
| 15.1 iris_dataset.csv.csv | 2.4 KB | ||
| 15.2 iris_with_answers.csv.csv | 3.63 KB | ||
| 15.3 Exercise - part 2.html | 134 B | ||
| 2. A Simple Example of Clustering.mp4 | 51.82 MB | ||
| 2. A Simple Example of Clustering.srt | 9.59 KB | ||
| 2.1 Example of clustering.html | 134 B | ||
| 3. A Simple Example of Clustering - Exercise.html | 87 B | ||
| 3.1 A simple example of clustering.html | 134 B | ||
| 3.2 Countries_exercise.csv.csv | 8.27 KB | ||
| 4. Clustering Categorical Data.mp4 | 21.23 MB | ||
| 4. Clustering Categorical Data.srt | 3.24 KB | ||
| 4.1 Clustering categorical data.html | 134 B | ||
| 5. Clustering Categorical Data - Exercise.html | 87 B | ||
| 5.1 Categorical.csv.csv | 10.34 KB | ||
| 5.2 Clustering categorical data.html | 134 B | ||
| 6. How to Choose the Number of Clusters.mp4 | 44.14 MB | ||
| 6. How to Choose the Number of Clusters.srt | 7.37 KB | ||
| 6.1 How to choose the number of clusters.html | 134 B | ||
| 7. How to Choose the Number of Clusters - Exercise.html | 87 B | ||
| 7.1 Countries_exercise.csv.csv | 8.27 KB | ||
| 7.2 How to choose the number of clusters.html | 134 B | ||
| 8. Pros and Cons of K-Means Clustering.mp4 | 37.7 MB | ||
| 8. Pros and Cons of K-Means Clustering.srt | 4.61 KB | ||
| 9. To Standardize or not to Standardize.mp4 | 30.11 MB | ||
| 9. To Standardize or not to Standardize.srt | 5.88 KB | ||
| 39. Advanced Statistical Methods - Other Types of Clustering | |||
| 1. Types of Clustering.mp4 | 44.58 MB | ||
| 1. Types of Clustering.srt | 4.66 KB | ||
| 2. Dendrogram.mp4 | 29.07 MB | ||
| 2. Dendrogram.srt | 7.36 KB | ||
| 3. Heatmaps.mp4 | 29.62 MB | ||
| 3. Heatmaps.srt | 6.34 KB | ||
| 3.1 Heatmaps.html | 134 B | ||
| 4. The Field of Data Science - The Benefits of Each Discipline | |||
| 1. The Reason behind these Disciplines.mp4 | 81.19 MB | ||
| 1. The Reason behind these Disciplines.srt | 6.5 KB | ||
| 2. The Reason behind these Disciplines.html | 165 B | ||
| 40. Part 6 Mathematics | |||
| 1. What is a matrix.mp4 | 33.59 MB | ||
| 1. What is a matrix.srt | 4.34 KB | ||
| 10. Addition and Subtraction of Matrices.mp4 | 32.61 MB | ||
| 10. Addition and Subtraction of Matrices.srt | 4.04 KB | ||
| 10.1 Addition and Subtraction of Matrices Python Notebook.html | 178 B | ||
| 11. Addition and Subtraction of Matrices.html | 165 B | ||
| 12. Errors when Adding Matrices.mp4 | 11.17 MB | ||
| 12. Errors when Adding Matrices.srt | 2.57 KB | ||
| 12.1 Errors when Adding Matrices Python Notebook.html | 220 B | ||
| 13. Transpose of a Matrix.mp4 | 38.08 MB | ||
| 13. Transpose of a Matrix.srt | 5.37 KB | ||
| 13.1 Transpose of a Matrix Python Notebook.html | 167 B | ||
| 14. Dot Product.mp4 | 24 MB | ||
| 14. Dot Product.srt | 4.26 KB | ||
| 14.1 Dot Product Python Notebook.html | 154 B | ||
| 15. Dot Product of Matrices.mp4 | 49.43 MB | ||
| 15. Dot Product of Matrices.srt | 9.52 KB | ||
| 15.1 Dot Product of Matrices Python Notebook.html | 171 B | ||
| 16. Why is Linear Algebra Useful.mp4 | 144.33 MB | ||
| 16. Why is Linear Algebra Useful.srt | 11.79 KB | ||
| 2. What is a Matrix.html | 165 B | ||
| 3. Scalars and Vectors.mp4 | 33.85 MB | ||
| 3. Scalars and Vectors.srt | 3.78 KB | ||
| 4. Scalars and Vectors.html | 165 B | ||
| 5. Linear Algebra and Geometry.mp4 | 49.79 MB | ||
| 5. Linear Algebra and Geometry.srt | 4.09 KB | ||
| 6. Linear Algebra and Geometry.html | 165 B | ||
| 7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 | 26.67 MB | ||
| 7. Arrays in Python - A Convenient Way To Represent Matrices.srt | 6.13 KB | ||
| 7.1 Arrays in Python Notebook.html | 181 B | ||
| 8. What is a Tensor.mp4 | 22.52 MB | ||
| 8. What is a Tensor.srt | 3.61 KB | ||
| 8.1 Tensors Notebook.html | 148 B | ||
| 9. What is a Tensor.html | 165 B | ||
| 41. Part 7 Deep Learning | |||
| 1. What to Expect from this Part.mp4 | 31.1 MB | ||
| 1. What to Expect from this Part.srt | 4.63 KB | ||
| 2. What is Machine Learning.html | 165 B | ||
| 42. Deep Learning - Introduction to Neural Networks | |||
| 1. Introduction to Neural Networks.mp4 | 42.92 MB | ||
| 1. Introduction to Neural Networks.srt | 5.9 KB | ||
| 1.1 Course Notes - Section 2.pdf.pdf | 578.08 KB | ||
| 10. The Linear Model with Multiple Inputs.html | 165 B | ||
| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 | 38.32 MB | ||
| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt | 5.46 KB | ||
| 12. The Linear model with Multiple Inputs and Multiple Outputs.html | 165 B | ||
| 13. Graphical Representation of Simple Neural Networks.mp4 | 22.64 MB | ||
| 13. Graphical Representation of Simple Neural Networks.srt | 2.69 KB | ||
| 14. Graphical Representation of Simple Neural Networks.html | 165 B | ||
| 15. What is the Objective Function.mp4 | 17.92 MB | ||
| 15. What is the Objective Function.srt | 2.12 KB | ||
| 16. What is the Objective Function.html | 165 B | ||
| 17. Common Objective Functions L2-norm Loss.mp4 | 23.28 MB | ||
| 17. Common Objective Functions L2-norm Loss.srt | 2.77 KB | ||
| 18. Common Objective Functions L2-norm Loss.html | 165 B | ||
| 19. Common Objective Functions Cross-Entropy Loss.mp4 | 37.25 MB | ||
| 19. Common Objective Functions Cross-Entropy Loss.srt | 5.25 KB | ||
| 2. Introduction to Neural Networks.html | 165 B | ||
| 20. Common Objective Functions Cross-Entropy Loss.html | 165 B | ||
| 21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 | 55.63 MB | ||
| 21. Optimization Algorithm 1-Parameter Gradient Descent.srt | 8.47 KB | ||
| 21.1 GD-function-example.xlsx.xlsx | 42.33 KB | ||
| 22. Optimization Algorithm 1-Parameter Gradient Descent.html | 165 B | ||
| 23. Optimization Algorithm n-Parameter Gradient Descent.mp4 | 39.43 MB | ||
| 23. Optimization Algorithm n-Parameter Gradient Descent.srt | 7.53 KB | ||
| 24. Optimization Algorithm n-Parameter Gradient Descent.html | 165 B | ||
| 3. Training the Model.mp4 | 28.71 MB | ||
| 3. Training the Model.srt | 4.28 KB | ||
| 3.1 Course Notes - Section 2.pdf.pdf | 578.08 KB | ||
| 4. Training the Model.html | 165 B | ||
| 5. Types of Machine Learning.mp4 | 45.1 MB | ||
| 5. Types of Machine Learning.srt | 5.23 KB | ||
| 6. Types of Machine Learning.html | 165 B | ||
| 7. The Linear Model (Linear Algebraic Version).mp4 | 28.45 MB | ||
| 7. The Linear Model (Linear Algebraic Version).srt | 3.88 KB | ||
| 8. The Linear Model.html | 165 B | ||
| 9. The Linear Model with Multiple Inputs.mp4 | 25.11 MB | ||
| 9. The Linear Model with Multiple Inputs.srt | 3.09 KB | ||
| 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy | |||
| 1. Basic NN Example (Part 1).mp4 | 20.6 MB | ||
| 1. Basic NN Example (Part 1).srt | 4.46 KB | ||
| 1.1 Bais NN Example Part 1.html | 136 B | ||
| 1.2 Shortcuts-for-Jupyter.pdf.pdf | 619.17 KB | ||
| 2. Basic NN Example (Part 2).mp4 | 34.94 MB | ||
| 2. Basic NN Example (Part 2).srt | 6.79 KB | ||
| 2.1 Basic NN Example (Part 2).html | 136 B | ||
| 3. Basic NN Example (Part 3).mp4 | 24.4 MB | ||
| 3. Basic NN Example (Part 3).srt | 4.46 KB | ||
| 3.1 Basic NN Example (Part 3).html | 136 B | ||
| 4. Basic NN Example (Part 4).mp4 | 61.13 MB | ||
| 4. Basic NN Example (Part 4).srt | 10.86 KB | ||
| 4.1 Basic NN Example (Part 4).html | 145 B | ||
| 5. Basic NN Example Exercises.html | 1.66 KB | ||
| 5.1 Basic NN Example Exercise 3b Solution.html | 154 B | ||
| 5.10 Basic NN Example Exercise 5 Solution.html | 149 B | ||
| 5.2 Basic NN Example Exercise 1 Solution.html | 149 B | ||
| 5.3 Basic NN Example Exercise 3a Solution.html | 154 B | ||
| 5.4 Basic NN Example Exercise 3d Solution.html | 154 B | ||
| 5.5 Basic NN Example Exercise 3c Solution.html | 154 B | ||
| 5.6 Basic NN Example (All Exercises).html | 143 B | ||
| 5.7 Basic NN Example Exercise 4 Solution.html | 149 B | ||
| 5.8 Basic NN Example Exercise 6 Solution.html | 149 B | ||
| 5.9 Basic NN Example Exercise 2 Solution.html | 149 B | ||
| 44. Deep Learning - TensorFlow 2.0 Introduction | |||
| 1. How to Install TensorFlow 2.0.mp4 | 38.76 MB | ||
| 1. How to Install TensorFlow 2.0.srt | 6.38 KB | ||
| 1.1 Shortcuts-for-Jupyter.pdf.pdf | 619.17 KB | ||
| 2. TensorFlow Outline and Comparison with Other Libraries.mp4 | 33.51 MB | ||
| 2. TensorFlow Outline and Comparison with Other Libraries.srt | 5.24 KB | ||
| 3. TensorFlow 1 vs TensorFlow 2.mp4 | 21.99 MB | ||
| 3. TensorFlow 1 vs TensorFlow 2.srt | 3.63 KB | ||
| 4. A Note on TensorFlow 2 Syntax.mp4 | 6.75 MB | ||
| 4. A Note on TensorFlow 2 Syntax.srt | 1.36 KB | ||
| 4.1 A note on TensorFlow 2 Syntax.html | 134 B | ||
| 5. Types of File Formats Supporting TensorFlow.mp4 | 16.4 MB | ||
| 5. Types of File Formats Supporting TensorFlow.srt | 3.5 KB | ||
| 5.1 Types of File Formats.html | 134 B | ||
| 6. Outlining the Model with TensorFlow 2.mp4 | 34.69 MB | ||
| 6. Outlining the Model with TensorFlow 2.srt | 7.83 KB | ||
| 6.1 Outlining the Model.html | 134 B | ||
| 7. Interpreting the Result and Extracting the Weights and Bias.mp4 | 30.27 MB | ||
| 7. Interpreting the Result and Extracting the Weights and Bias.srt | 6.23 KB | ||
| 7.1 Interpreting the Result.html | 134 B | ||
| 8. Customizing a TensorFlow 2 Model.mp4 | 22.91 MB | ||
| 8. Customizing a TensorFlow 2 Model.srt | 4.11 KB | ||
| 8.1 Customizing a TensorFlow 2 Model.html | 134 B | ||
| 9. Basic NN with TensorFlow Exercises.html | 1.29 KB | ||
| 9.1 Basic NN with TensorFlow.html | 134 B | ||
| 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks | |||
| 1. What is a Layer.mp4 | 12.5 MB | ||
| 1. What is a Layer.srt | 2.39 KB | ||
| 1.1 Course Notes - Section 6.pdf.pdf | 936.42 KB | ||
| 2. What is a Deep Net.mp4 | 29.53 MB | ||
| 2. What is a Deep Net.srt | 3.24 KB | ||
| 2.1 Course Notes - Section 6.pdf.pdf | 936.42 KB | ||
| 3. Digging into a Deep Net.mp4 | 59.36 MB | ||
| 3. Digging into a Deep Net.srt | 6.7 KB | ||
| 4. Non-Linearities and their Purpose.mp4 | 27.68 MB | ||
| 4. Non-Linearities and their Purpose.srt | 3.88 KB | ||
| 5. Activation Functions.mp4 | 25.09 MB | ||
| 5. Activation Functions.srt | 5.25 KB | ||
| 6. Activation Functions Softmax Activation.mp4 | 25.92 MB | ||
| 6. Activation Functions Softmax Activation.srt | 4.46 KB | ||
| 7. Backpropagation.mp4 | 34.95 MB | ||
| 7. Backpropagation.srt | 4.46 KB | ||
| 8. Backpropagation picture.mp4 | 19.5 MB | ||
| 8. Backpropagation picture.srt | 3.97 KB | ||
| 9. Backpropagation - A Peek into the Mathematics of Optimization.html | 539 B | ||
| 9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf | 182.36 KB | ||
| 46. Deep Learning - Overfitting | |||
| 1. What is Overfitting.mp4 | 31.09 MB | ||
| 1. What is Overfitting.srt | 5.58 KB | ||
| 2. Underfitting and Overfitting for Classification.mp4 | 25.07 MB | ||
| 2. Underfitting and Overfitting for Classification.srt | 2.63 KB | ||
| 3. What is Validation.mp4 | 32.71 MB | ||
| 3. What is Validation.srt | 4.9 KB | ||
| 4. Training, Validation, and Test Datasets.mp4 | 25.19 MB | ||
| 4. Training, Validation, and Test Datasets.srt | 3.6 KB | ||
| 5. N-Fold Cross Validation.mp4 | 20.7 MB | ||
| 5. N-Fold Cross Validation.srt | 4.17 KB | ||
| 6. Early Stopping or When to Stop Training.mp4 | 24.17 MB | ||
| 6. Early Stopping or When to Stop Training.srt | 6.86 KB | ||
| 47. Deep Learning - Initialization | |||
| 1. What is Initialization.mp4 | 21.76 MB | ||
| 1. What is Initialization.srt | 3.5 KB | ||
| 2. Types of Simple Initializations.mp4 | 14.32 MB | ||
| 2. Types of Simple Initializations.srt | 3.67 KB | ||
| 3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 | 17.14 MB | ||
| 3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt | 3.71 KB | ||
| 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules | |||
| 1. Stochastic Gradient Descent.mp4 | 28.68 MB | ||
| 1. Stochastic Gradient Descent.srt | 4.81 KB | ||
| 2. Problems with Gradient Descent.mp4 | 11.01 MB | ||
| 2. Problems with Gradient Descent.srt | 2.83 KB | ||
| 3. Momentum.mp4 | 16.43 MB | ||
| 3. Momentum.srt | 3.45 KB | ||
| 4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 | 29.08 MB | ||
| 4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt | 5.93 KB | ||
| 5. Learning Rate Schedules Visualized.mp4 | 9.12 MB | ||
| 5. Learning Rate Schedules Visualized.srt | 2.16 KB | ||
| 6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 | 26.35 MB | ||
| 6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt | 5.21 KB | ||
| 7. Adam (Adaptive Moment Estimation).mp4 | 22.36 MB | ||
| 7. Adam (Adaptive Moment Estimation).srt | 3.33 KB | ||
| 49. Deep Learning - Preprocessing | |||
| 1. Preprocessing Introduction.mp4 | 27.78 MB | ||
| 1. Preprocessing Introduction.srt | 3.87 KB | ||
| 2. Types of Basic Preprocessing.mp4 | 11.84 MB | ||
| 2. Types of Basic Preprocessing.srt | 1.63 KB | ||
| 3. Standardization.mp4 | 50.99 MB | ||
| 3. Standardization.srt | 5.97 KB | ||
| 4. Preprocessing Categorical Data.mp4 | 18.61 MB | ||
| 4. Preprocessing Categorical Data.srt | 2.76 KB | ||
| 5. Binary and One-Hot Encoding.mp4 | 28.95 MB | ||
| 5. Binary and One-Hot Encoding.srt | 4.81 KB | ||
| 5. The Field of Data Science - Popular Data Science Techniques | |||
| 1. Techniques for Working with Traditional Data.mp4 | 138.31 MB | ||
| 1. Techniques for Working with Traditional Data.srt | 10.62 KB | ||
| 10. Techniques for Working with Traditional Methods.mp4 | 111.66 MB | ||
| 10. Techniques for Working with Traditional Methods.srt | 10.98 KB | ||
| 11. Techniques for Working with Traditional Methods.html | 165 B | ||
| 12. Real Life Examples of Traditional Methods.mp4 | 42.78 MB | ||
| 12. Real Life Examples of Traditional Methods.srt | 3.58 KB | ||
| 13. Machine Learning (ML) Techniques.mp4 | 99.32 MB | ||
| 13. Machine Learning (ML) Techniques.srt | 8.73 KB | ||
| 14. Machine Learning (ML) Techniques.html | 165 B | ||
| 15. Types of Machine Learning.mp4 | 125.15 MB | ||
| 15. Types of Machine Learning.srt | 10.51 KB | ||
| 16. Types of Machine Learning.html | 165 B | ||
| 17. Real Life Examples of Machine Learning (ML).mp4 | 36.82 MB | ||
| 17. Real Life Examples of Machine Learning (ML).srt | 2.9 KB | ||
| 18. Real Life Examples of Machine Learning (ML).html | 165 B | ||
| 2. Techniques for Working with Traditional Data.html | 165 B | ||
| 3. Real Life Examples of Traditional Data.mp4 | 29.93 MB | ||
| 3. Real Life Examples of Traditional Data.srt | 2.24 KB | ||
| 4. Techniques for Working with Big Data.mp4 | 75.5 MB | ||
| 4. Techniques for Working with Big Data.srt | 5.67 KB | ||
| 5. Techniques for Working with Big Data.html | 165 B | ||
| 6. Real Life Examples of Big Data.mp4 | 22.04 MB | ||
| 6. Real Life Examples of Big Data.srt | 1.88 KB | ||
| 7. Business Intelligence (BI) Techniques.mp4 | 89.94 MB | ||
| 7. Business Intelligence (BI) Techniques.srt | 8.63 KB | ||
| 8. Business Intelligence (BI) Techniques.html | 165 B | ||
| 9. Real Life Examples of Business Intelligence (BI).mp4 | 29.54 MB | ||
| 9. Real Life Examples of Business Intelligence (BI).srt | 2.13 KB | ||
| 50. Deep Learning - Classifying on the MNIST Dataset | |||
| 1. MNIST The Dataset.mp4 | 13.39 MB | ||
| 1. MNIST The Dataset.srt | 3.58 KB | ||
| 10. MNIST Learning.mp4 | 40.96 MB | ||
| 10. MNIST Learning.srt | 7.94 KB | ||
| 10.1 MNIST Learning.html | 134 B | ||
| 11. MNIST - Exercises.html | 1.98 KB | ||
| 11.1 MNIST - Exercises.html | 134 B | ||
| 12. MNIST Testing the Model.mp4 | 29.53 MB | ||
| 12. MNIST Testing the Model.srt | 6.02 KB | ||
| 12.1 MNIST Testing the Model.html | 134 B | ||
| 2. MNIST How to Tackle the MNIST.mp4 | 18.67 MB | ||
| 2. MNIST How to Tackle the MNIST.srt | 3.52 KB | ||
| 3. MNIST Importing the Relevant Packages and Loading the Data.mp4 | 16.32 MB | ||
| 3. MNIST Importing the Relevant Packages and Loading the Data.srt | 3.07 KB | ||
| 3.1 MNIST Importing the Relevant Packages.html | 134 B | ||
| 4. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 | 29.05 MB | ||
| 4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt | 6.27 KB | ||
| 5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html | 79 B | ||
| 5.1 MNIST Preprocess the Data.html | 134 B | ||
| 6. MNIST Preprocess the Data - Shuffle and Batch.mp4 | 41.52 MB | ||
| 6. MNIST Preprocess the Data - Shuffle and Batch.srt | 9.26 KB | ||
| 7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html | 79 B | ||
| 7.1 MNIST Preprocess the Data.html | 134 B | ||
| 8. MNIST Outline the Model.mp4 | 28.23 MB | ||
| 8. MNIST Outline the Model.srt | 7.2 KB | ||
| 8.1 MNIST Outline the Model.html | 134 B | ||
| 9. MNIST Select the Loss and the Optimizer.mp4 | 13.91 MB | ||
| 9. MNIST Select the Loss and the Optimizer.srt | 3.02 KB | ||
| 9.1 MNIST Select the Loss and the Optimizer.html | 134 B | ||
| 51. Deep Learning - Business Case Example | |||
| 1. Business Case Exploring the Dataset and Identifying Predictors.mp4 | 66.28 MB | ||
| 1. Business Case Exploring the Dataset and Identifying Predictors.srt | 10.66 KB | ||
| 1.1 Audiobooks_data.csv.csv | 710.77 KB | ||
| 1.2 Business Case Exploring the Dataset.html | 134 B | ||
| 10. Setting an Early Stopping Mechanism - Exercise.html | 192 B | ||
| 11. Business Case Testing the Model.mp4 | 10.8 MB | ||
| 11. Business Case Testing the Model.srt | 2.04 KB | ||
| 11.1 Business Case Testing the Model.html | 134 B | ||
| 12. Business Case Final Exercise.html | 433 B | ||
| 12.1 Business Case Final Exercise.html | 134 B | ||
| 2. Business Case Outlining the Solution.mp4 | 7.31 MB | ||
| 2. Business Case Outlining the Solution.srt | 2 KB | ||
| 3. Business Case Balancing the Dataset.mp4 | 30.44 MB | ||
| 3. Business Case Balancing the Dataset.srt | 4.5 KB | ||
| 4. Business Case Preprocessing the Data.mp4 | 84.33 MB | ||
| 4. Business Case Preprocessing the Data.srt | 12.3 KB | ||
| 4.1 Business Case Preprocessing the Data.html | 134 B | ||
| 5. Business Case Preprocessing the Data - Exercise.html | 370 B | ||
| 5.1 Business Case Preprocessing the Data.html | 134 B | ||
| 6. Business Case Load the Preprocessed Data.mp4 | 17.57 MB | ||
| 6. Business Case Load the Preprocessed Data.srt | 4.7 KB | ||
| 7. Business Case Load the Preprocessed Data - Exercise.html | 79 B | ||
| 7.1 Business Case Load the Preprocessed Data.html | 134 B | ||
| 8. Business Case Learning and Interpreting the Result.mp4 | 31.18 MB | ||
| 8. Business Case Learning and Interpreting the Result.srt | 6.27 KB | ||
| 8.1 Business Case Learning and Interpreting.html | 134 B | ||
| 9. Business Case Setting an Early Stopping Mechanism.mp4 | 49.81 MB | ||
| 9. Business Case Setting an Early Stopping Mechanism.srt | 7.82 KB | ||
| 9.1 Business Case Setting an Early Stopping Mechanism.html | 134 B | ||
| 52. Deep Learning - Conclusion | |||
| 1. Summary on What You've Learned.mp4 | 39.75 MB | ||
| 1. Summary on What You've Learned.srt | 5.21 KB | ||
| 2. What's Further out there in terms of Machine Learning.mp4 | 20.12 MB | ||
| 2. What's Further out there in terms of Machine Learning.srt | 2.55 KB | ||
| 3. DeepMind and Deep Learning.html | 1.05 KB | ||
| 4. An overview of CNNs.mp4 | 58.79 MB | ||
| 4. An overview of CNNs.srt | 6.44 KB | ||
| 5. An Overview of RNNs.mp4 | 25.26 MB | ||
| 5. An Overview of RNNs.srt | 3.71 KB | ||
| 6. An Overview of non-NN Approaches.mp4 | 44.78 MB | ||
| 6. An Overview of non-NN Approaches.srt | 5.12 KB | ||
| 53. Appendix Deep Learning - TensorFlow 1 Introduction | |||
| 1. READ ME!!!!.html | 564 B | ||
| 10. Basic NN Example with TF Exercises.html | 1.59 KB | ||
| 10.1 Basic NN Example with TensorFlow Exercise 2.3 Solution.html | 162 B | ||
| 10.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html | 162 B | ||
| 10.3 Basic NN Example with TensorFlow Exercise 3 Solution.html | 160 B | ||
| 10.4 Basic NN Example with TensorFlow Exercise 1 Solution.html | 160 B | ||
| 10.5 Basic NN Example with TensorFlow (All Exercises).html | 154 B | ||
| 10.6 Basic NN Example with TensorFlow Exercise 4 Solution.html | 160 B | ||
| 10.7 Basic NN Example with TensorFlow Exercise 2.2 Solution.html | 162 B | ||
| 10.8 Basic NN Example with TensorFlow Exercise 2.4 Solution.html | 162 B | ||
| 2. How to Install TensorFlow 1.mp4 | 11.35 MB | ||
| 2. How to Install TensorFlow 1.srt | 3.42 KB | ||
| 3. A Note on Installing Packages in Anaconda.html | 2.3 KB | ||
| 4. TensorFlow Intro.mp4 | 47.7 MB | ||
| 4. TensorFlow Intro.srt | 5.2 KB | ||
| 5. Actual Introduction to TensorFlow.mp4 | 17.41 MB | ||
| 5. Actual Introduction to TensorFlow.srt | 2.17 KB | ||
| 5.1 Actual Introduction to TensorFlow.html | 134 B | ||
| 5.2 Shortcuts-for-Jupyter.pdf.pdf | 619.17 KB | ||
| 6. Types of File Formats, supporting Tensors.mp4 | 20.35 MB | ||
| 6. Types of File Formats, supporting Tensors.srt | 3.45 KB | ||
| 6.1 Basic NN Example with TensorFlow (Part 1).html | 154 B | ||
| 7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 | 38.5 MB | ||
| 7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt | 7.36 KB | ||
| 7.1 Basic NN Example with TensorFlow (Part 2).html | 154 B | ||
| 8. Basic NN Example with TF Loss Function and Gradient Descent.mp4 | 32.51 MB | ||
| 8. Basic NN Example with TF Loss Function and Gradient Descent.srt | 4.83 KB | ||
| 8.1 Basic NN Example with TensorFlow (Part 3).html | 154 B | ||
| 9. Basic NN Example with TF Model Output.mp4 | 37.39 MB | ||
| 9. Basic NN Example with TF Model Output.srt | 7.93 KB | ||
| 9.1 Basic NN Example with TensorFlow (Complete).html | 156 B | ||
| 54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset | |||
| 1. MNIST What is the MNIST Dataset.mp4 | 17.82 MB | ||
| 1. MNIST What is the MNIST Dataset.srt | 3.49 KB | ||
| 10. MNIST Solutions.html | 2.19 KB | ||
| 10.1 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html | 162 B | ||
| 10.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html | 172 B | ||
| 10.11 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html | 165 B | ||
| 10.2 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html | 165 B | ||
| 10.3 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html | 172 B | ||
| 10.4 TensorFlow MNIST '2. Depth' Solution.html | 150 B | ||
| 10.5 TensorFlow MNIST 'Time' Solution.html | 162 B | ||
| 10.6 TensorFlow MNIST '1. Width' Solution.html | 150 B | ||
| 10.7 TensorFlow MNIST 'Around 98% Accuracy' Solution.html | 157 B | ||
| 10.8 TensorFlow MNIST '3. Width and Depth' Solution.html | 160 B | ||
| 10.9 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html | 162 B | ||
| 11. MNIST Exercises.html | 2.13 KB | ||
| 11.1 TensorFlow MNIST All Exercises.html | 144 B | ||
| 2. MNIST How to Tackle the MNIST.mp4 | 22.58 MB | ||
| 2. MNIST How to Tackle the MNIST.srt | 3.62 KB | ||
| 3. MNIST Relevant Packages.mp4 | 18.9 MB | ||
| 3. MNIST Relevant Packages.srt | 2.12 KB | ||
| 3.1 TensorFlow MNIST Part 1 with Comments.html | 159 B | ||
| 4. MNIST Model Outline.mp4 | 56.38 MB | ||
| 4. MNIST Model Outline.srt | 9.06 KB | ||
| 4.1 TensorFlow MNIST Part 2 with Comments.html | 159 B | ||
| 5. MNIST Loss and Optimization Algorithm.mp4 | 25.86 MB | ||
| 5. MNIST Loss and Optimization Algorithm.srt | 3.53 KB | ||
| 5.1 TensorFlow MNIST Part 3 with Comments.html | 159 B | ||
| 6. Calculating the Accuracy of the Model.mp4 | 43.9 MB | ||
| 6. Calculating the Accuracy of the Model.srt | 5.19 KB | ||
| 6.1 TensorFlow MNIST Part 4 with Comments.html | 159 B | ||
| 7. MNIST Batching and Early Stopping.mp4 | 12.86 MB | ||
| 7. MNIST Batching and Early Stopping.srt | 2.92 KB | ||
| 7.1 TensorFlow MNIST Part 5 with Comments.html | 159 B | ||
| 8. MNIST Learning.mp4 | 46.68 MB | ||
| 8. MNIST Learning.srt | 10.19 KB | ||
| 8.1 TensorFlow MNIST Part 6 with Comments.html | 159 B | ||
| 9. MNIST Results and Testing.mp4 | 62.77 MB | ||
| 9. MNIST Results and Testing.srt | 8.17 KB | ||
| 9.1 TensorFlow MNIST Complete Code with Comments.html | 152 B | ||
| 55. Appendix Deep Learning - TensorFlow 1 Business Case | |||
| 1. Business Case Getting acquainted with the dataset.mp4 | 87.66 MB | ||
| 1. Business Case Getting acquainted with the dataset.srt | 10.78 KB | ||
| 1.1 Audiobooks_data.csv.csv | 710.77 KB | ||
| 10. Business Case Testing the Model.mp4 | 11.2 MB | ||
| 10. Business Case Testing the Model.srt | 2.71 KB | ||
| 11. Business Case A Comment on the Homework.mp4 | 36.39 MB | ||
| 11. Business Case A Comment on the Homework.srt | 5.3 KB | ||
| 11.1 TensorFlow Business Case Homework.html | 134 B | ||
| 12. Business Case Final Exercise.html | 439 B | ||
| 12.1 TensorFlow Business Case Homework.html | 134 B | ||
| 2. Business Case Outlining the Solution.mp4 | 12.21 MB | ||
| 2. Business Case Outlining the Solution.srt | 2.52 KB | ||
| 3. The Importance of Working with a Balanced Dataset.mp4 | 39.41 MB | ||
| 3. The Importance of Working with a Balanced Dataset.srt | 4.48 KB | ||
| 4. Business Case Preprocessing.mp4 | 103.42 MB | ||
| 4. Business Case Preprocessing.srt | 13.45 KB | ||
| 4.1 Audiobooks Preprocessing.html | 134 B | ||
| 5. Business Case Preprocessing Exercise.html | 383 B | ||
| 5.1 Preprocessing Exercise.html | 134 B | ||
| 6. Creating a Data Provider.mp4 | 76.34 MB | ||
| 6. Creating a Data Provider.srt | 7.75 KB | ||
| 6.1 Creating a Data Provider (Class).html | 134 B | ||
| 7. Business Case Model Outline.mp4 | 53.12 MB | ||
| 7. Business Case Model Outline.srt | 6.94 KB | ||
| 7.1 TensorFlow Business Case Model Outline.html | 134 B | ||
| 8. Business Case Optimization.mp4 | 41.52 MB | ||
| 8. Business Case Optimization.srt | 6.6 KB | ||
| 8.1 TensorFlow Business Case Optimization.html | 134 B | ||
| 9. Business Case Interpretation.mp4 | 25.74 MB | ||
| 9. Business Case Interpretation.srt | 2.94 KB | ||
| 9.1 TensorFlow Business Case Interpretation.html | 134 B | ||
| 56. Software Integration | |||
| 1. What are Data, Servers, Clients, Requests, and Responses.mp4 | 69.03 MB | ||
| 1. What are Data, Servers, Clients, Requests, and Responses.srt | 5.93 KB | ||
| 10. Software Integration - Explained.html | 165 B | ||
| 2. What are Data, Servers, Clients, Requests, and Responses.html | 165 B | ||
| 3. What are Data Connectivity, APIs, and Endpoints.mp4 | 104.08 MB | ||
| 3. What are Data Connectivity, APIs, and Endpoints.srt | 8.54 KB | ||
| 4. What are Data Connectivity, APIs, and Endpoints.html | 165 B | ||
| 5. Taking a Closer Look at APIs.mp4 | 115.59 MB | ||
| 5. Taking a Closer Look at APIs.srt | 10.39 KB | ||
| 6. Taking a Closer Look at APIs.html | 165 B | ||
| 7. Communication between Software Products through Text Files.mp4 | 60.34 MB | ||
| 7. Communication between Software Products through Text Files.srt | 5.47 KB | ||
| 8. Communication between Software Products through Text Files.html | 165 B | ||
| 9. Software Integration - Explained.mp4 | 63.69 MB | ||
| 9. Software Integration - Explained.srt | 6.71 KB | ||
| 57. Case Study - What's Next in the Course | |||
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 | 52.3 MB | ||
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt | 5.46 KB | ||
| 2. The Business Task.mp4 | 39.15 MB | ||
| 2. The Business Task.srt | 3.74 KB | ||
| 3. Introducing the Data Set.mp4 | 40.87 MB | ||
| 3. Introducing the Data Set.srt | 4.15 KB | ||
| 4. Introducing the Data Set.html | 165 B | ||
| 58. Case Study - Preprocessing the 'Absenteeism_data' | |||
| 1. What to Expect from the Following Sections.html | 2.48 KB | ||
| 1.1 Absenteeism_data.csv.csv | 32.05 KB | ||
| 1.2 data_preprocessing_homework.pdf.pdf | 134.47 KB | ||
| 1.3 df_preprocessed.csv.csv | 29.11 KB | ||
| 10. Analyzing the Reasons for Absence.mp4 | 40.57 MB | ||
| 10. Analyzing the Reasons for Absence.srt | 5.85 KB | ||
| 11. Obtaining Dummies from a Single Feature.mp4 | 81.11 MB | ||
| 11. Obtaining Dummies from a Single Feature.srt | 10.2 KB | ||
| 12. EXERCISE - Obtaining Dummies from a Single Feature.html | 129 B | ||
| 13. SOLUTION - Obtaining Dummies from a Single Feature.html | 116 B | ||
| 14. Dropping a Dummy Variable from the Data Set.html | 2.34 KB | ||
| 15. More on Dummy Variables A Statistical Perspective.mp4 | 13.75 MB | ||
| 15. More on Dummy Variables A Statistical Perspective.srt | 1.7 KB | ||
| 16. Classifying the Various Reasons for Absence.mp4 | 74.61 MB | ||
| 16. Classifying the Various Reasons for Absence.srt | 10.02 KB | ||
| 17. Using .concat() in Python.mp4 | 38.73 MB | ||
| 17. Using .concat() in Python.srt | 5.07 KB | ||
| 18. EXERCISE - Using .concat() in Python.html | 189 B | ||
| 19. SOLUTION - Using .concat() in Python.html | 142 B | ||
| 2. Importing the Absenteeism Data in Python.mp4 | 23.15 MB | ||
| 2. Importing the Absenteeism Data in Python.srt | 3.99 KB | ||
| 20. Reordering Columns in a Pandas DataFrame in Python.mp4 | 14.02 MB | ||
| 20. Reordering Columns in a Pandas DataFrame in Python.srt | 1.82 KB | ||
| 21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html | 167 B | ||
| 22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html | 471 B | ||
| 23. Creating Checkpoints while Coding in Jupyter.mp4 | 25.67 MB | ||
| 23. Creating Checkpoints while Coding in Jupyter.srt | 3.64 KB | ||
| 23.1 Creating Checkpoints.html | 176 B | ||
| 24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html | 137 B | ||
| 25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html | 117 B | ||
| 26. Analyzing the Dates from the Initial Data Set.mp4 | 57.28 MB | ||
| 26. Analyzing the Dates from the Initial Data Set.srt | 8.43 KB | ||
| 27. Extracting the Month Value from the Date Column.mp4 | 47.79 MB | ||
| 27. Extracting the Month Value from the Date Column.srt | 7.97 KB | ||
| 28. Extracting the Day of the Week from the Date Column.mp4 | 27.96 MB | ||
| 28. Extracting the Day of the Week from the Date Column.srt | 4.47 KB | ||
| 29. EXERCISE - Removing the Date Column.html | 1.21 KB | ||
| 29.1 Removing the “Date” Column.html | 188 B | ||
| 29.2 Preprocessing.html | 191 B | ||
| 3. Checking the Content of the Data Set.mp4 | 61.91 MB | ||
| 3. Checking the Content of the Data Set.srt | 7.04 KB | ||
| 30. Analyzing Several Straightforward Columns for this Exercise.mp4 | 29.52 MB | ||
| 30. Analyzing Several Straightforward Columns for this Exercise.srt | 4.34 KB | ||
| 31. Working on Education, Children, and Pets.mp4 | 39.59 MB | ||
| 31. Working on Education, Children, and Pets.srt | 5.67 KB | ||
| 32. Final Remarks of this Section.mp4 | 21.63 MB | ||
| 32. Final Remarks of this Section.srt | 2.47 KB | ||
| 32.1 Exercises and solutions.html | 170 B | ||
| 32.2 Preprocessing.html | 156 B | ||
| 33. A Note on Exporting Your Data as a .csv File.html | 883 B | ||
| 4. Introduction to Terms with Multiple Meanings.mp4 | 27.85 MB | ||
| 4. Introduction to Terms with Multiple Meanings.srt | 4.05 KB | ||
| 5. What's Regression Analysis - a Quick Refresher.html | 2.84 KB | ||
| 6. Using a Statistical Approach towards the Solution to the Exercise.mp4 | 20.19 MB | ||
| 6. Using a Statistical Approach towards the Solution to the Exercise.srt | 2.8 KB | ||
| 7. Dropping a Column from a DataFrame in Python.mp4 | 61.76 MB | ||
| 7. Dropping a Column from a DataFrame in Python.srt | 7.81 KB | ||
| 8. EXERCISE - Dropping a Column from a DataFrame in Python.html | 866 B | ||
| 9. SOLUTION - Dropping a Column from a DataFrame in Python.html | 113 B | ||
| 59. Case Study - Applying Machine Learning to Create the 'absenteeism_module' | |||
| 1. Exploring the Problem with a Machine Learning Mindset.mp4 | 27.54 MB | ||
| 1. Exploring the Problem with a Machine Learning Mindset.srt | 4.58 KB | ||
| 1.1 Absenteeism_preprocessed.csv.csv | 29.13 KB | ||
| 10. Interpreting the Coefficients of the Logistic Regression.mp4 | 40.41 MB | ||
| 10. Interpreting the Coefficients of the Logistic Regression.srt | 7.25 KB | ||
| 11. Backward Elimination or How to Simplify Your Model.mp4 | 39.56 MB | ||
| 11. Backward Elimination or How to Simplify Your Model.srt | 5.24 KB | ||
| 11.1 Logistic Regression prior to Backward Elimination.html | 226 B | ||
| 12. Testing the Model We Created.mp4 | 49.06 MB | ||
| 12. Testing the Model We Created.srt | 6.5 KB | ||
| 13. Saving the Model and Preparing it for Deployment.mp4 | 37.45 MB | ||
| 13. Saving the Model and Preparing it for Deployment.srt | 5.57 KB | ||
| 14. ARTICLE - A Note on 'pickling'.html | 2.14 KB | ||
| 15. EXERCISE - Saving the Model (and Scaler).html | 284 B | ||
| 15.1 Logistic Regression with Comments.html | 210 B | ||
| 15.2 Logistic Regression.html | 196 B | ||
| 16. Preparing the Deployment of the Model through a Module.mp4 | 44.49 MB | ||
| 16. Preparing the Deployment of the Model through a Module.srt | 5.62 KB | ||
| 2. Creating the Targets for the Logistic Regression.mp4 | 45.8 MB | ||
| 2. Creating the Targets for the Logistic Regression.srt | 8.39 KB | ||
| 3. Selecting the Inputs for the Logistic Regression.mp4 | 16.75 MB | ||
| 3. Selecting the Inputs for the Logistic Regression.srt | 3.66 KB | ||
| 4. Standardizing the Data.mp4 | 20.6 MB | ||
| 4. Standardizing the Data.srt | 4.19 KB | ||
| 5. Splitting the Data for Training and Testing.mp4 | 52.76 MB | ||
| 5. Splitting the Data for Training and Testing.srt | 8.1 KB | ||
| 6. Fitting the Model and Assessing its Accuracy.mp4 | 41.62 MB | ||
| 6. Fitting the Model and Assessing its Accuracy.srt | 7.38 KB | ||
| 7. Creating a Summary Table with the Coefficients and Intercept.mp4 | 38.87 MB | ||
| 7. Creating a Summary Table with the Coefficients and Intercept.srt | 6.62 KB | ||
| 8. Interpreting the Coefficients for Our Problem.mp4 | 52.37 MB | ||
| 8. Interpreting the Coefficients for Our Problem.srt | 7.89 KB | ||
| 9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 | 41.19 MB | ||
| 9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt | 5.03 KB | ||
| 9.1 Logistic Regression prior to Custom Scaler.html | 219 B | ||
| 6. The Field of Data Science - Popular Data Science Tools | |||
| 1. Necessary Programming Languages and Software Used in Data Science.mp4 | 103.51 MB | ||
| 1. Necessary Programming Languages and Software Used in Data Science.srt | 7.29 KB | ||
| 2. Necessary Programming Languages and Software Used in Data Science.html | 165 B | ||
| 60. Case Study - Loading the 'absenteeism_module' | |||
| 1. Are You Sure You're All Set.html | 519 B | ||
| 1.1 5 Files Needed to Deploy the Model.html | 134 B | ||
| 2. Deploying the 'absenteeism_module' - Part I.mp4 | 25.48 MB | ||
| 2. Deploying the 'absenteeism_module' - Part I.srt | 4.76 KB | ||
| 3. Deploying the 'absenteeism_module' - Part II.mp4 | 54.26 MB | ||
| 3. Deploying the 'absenteeism_module' - Part II.srt | 7.53 KB | ||
| 4. Exporting the Obtained Data Set as a .csv.html | 998 B | ||
| 4.1 Deploying the ‘absenteeism_module.html | 185 B | ||
| 61. Case Study - Analyzing the Predicted Outputs in Tableau | |||
| 1. EXERCISE - Age vs Probability.html | 385 B | ||
| 2. Analyzing Age vs Probability in Tableau.mp4 | 56.55 MB | ||
| 2. Analyzing Age vs Probability in Tableau.srt | 10.01 KB | ||
| 3. EXERCISE - Reasons vs Probability.html | 397 B | ||
| 4. Analyzing Reasons vs Probability in Tableau.mp4 | 59.33 MB | ||
| 4. Analyzing Reasons vs Probability in Tableau.srt | 9.54 KB | ||
| 5. EXERCISE - Transportation Expense vs Probability.html | 553 B | ||
| 6. Analyzing Transportation Expense vs Probability in Tableau.mp4 | 40.63 MB | ||
| 6. Analyzing Transportation Expense vs Probability in Tableau.srt | 7.21 KB | ||
| 62. Bonus lecture | |||
| 1. Bonus Lecture Next Steps.html | 2.56 KB | ||
| 7. The Field of Data Science - Careers in Data Science | |||
| 1. Finding the Job - What to Expect and What to Look for.mp4 | 54.38 MB | ||
| 1. Finding the Job - What to Expect and What to Look for.srt | 4.49 KB | ||
| 2. Finding the Job - What to Expect and What to Look for.html | 165 B | ||
| 8. The Field of Data Science - Debunking Common Misconceptions | |||
| 1. Debunking Common Misconceptions.mp4 | 72.85 MB | ||
| 1. Debunking Common Misconceptions.srt | 5.29 KB | ||
| 2. Debunking Common Misconceptions.html | 165 B | ||
| 9. Part 2 Probability | |||
| 1. The Basic Probability Formula.mp4 | 85.91 MB | ||
| 1. The Basic Probability Formula.srt | 8.9 KB | ||
| 1.1 Course Notes - Basic Probability.pdf.pdf | 371.05 KB | ||
| 2. The Basic Probability Formula.html | 165 B | ||
| 3. Computing Expected Values.mp4 | 75.68 MB | ||
| 3. Computing Expected Values.srt | 6.68 KB | ||
| 4. Computing Expected Values.html | 165 B | ||
| 5. Frequency.mp4 | 61.74 MB | ||
| 5. Frequency.srt | 6.42 KB | ||
| 6. Frequency.html | 165 B | ||
| 7. Events and Their Complements.mp4 | 59.15 MB | ||
| 7. Events and Their Complements.srt | 6.71 KB | ||
| 8. Events and Their Complements.html | 165 B | ||
| [FreeAllCourse.Com].URL | 228 B | ||
| ▲ 1327 total files | |||
Description
The Data Science Course 2020 Complete Data Science Bootcamp 
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
What you'll learn
The course provides the entire toolbox you need to become a data scientist
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
Impress interviewers by showing an understanding of the data science field
Learn how to pre-process data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Created by 365 Careers, 365 Careers Team
Last updated 1/2020
English
Get Updated Course Only On: FreeAllCourse.Com
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