Udemy - Bayesian Machine Learning in Python - A - B Testing

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Udemy - Bayesian Machine Learning in Python - A - B Testing (Size: 2.6 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Introduction and Outline
  1. What's this course all about.en_US.srt 5.2 KB
  1. What's this course all about.mp4 18.2 MB
  10 - Extra Help With Python Coding for Beginners (FAQ by Student Request)
  11 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)
  12 - Appendix FAQ Finale
  2 - The High-Level Picture
  3 - Bayes Rule and Probability Review
  10. The Monty Hall Problem.en_US.srt 8.4 KB
  10. The Monty Hall Problem.mp4 15.8 MB
  11. Maximum Likelihood Estimation - Bernoulli.en_US.srt 12.9 KB
  11. Maximum Likelihood Estimation - Bernoulli.mp4 24.4 MB
  12. Click-Through Rates (CTR).en_US.srt 2.3 KB
  12. Click-Through Rates (CTR).mp4 5.2 MB
  13. Maximum Likelihood Estimation - Gaussian (pt 1).en_US.srt 11.4 KB
  13. Maximum Likelihood Estimation - Gaussian (pt 1).mp4 22.9 MB
  14. Maximum Likelihood Estimation - Gaussian (pt 2).en_US.srt 9.2 KB
  14. Maximum Likelihood Estimation - Gaussian (pt 2).mp4 18 MB
  15. CDFs and Percentiles.en_US.srt 10.1 KB
  15. CDFs and Percentiles.mp4 21.9 MB
  16. Probability Review in Code.en_US.srt 11.3 KB
  16. Probability Review in Code.mp4 33 MB
  17. Probability Review Section Summary.en_US.srt 6.4 KB
  17. Probability Review Section Summary.mp4 13.7 MB
  18. Remedial Statistics vs Machine Learning.en_US.srt 8.2 KB
  18. Remedial Statistics vs Machine Learning.mp4 19.8 MB
  19. Suggestion Box.en_US.srt 4.1 KB
  19. Suggestion Box.mp4 39.9 MB
  4 - Traditional A B Testing
  20. Confidence Intervals (pt 1) - Intuition.en_US.srt 5.5 KB
  20. Confidence Intervals (pt 1) - Intuition.mp4 12.8 MB
  21. Confidence Intervals (pt 2) - Beginner Level.en_US.srt 5.2 KB
  21. Confidence Intervals (pt 2) - Beginner Level.mp4 11.9 MB
  22. Confidence Intervals (pt 3) - Intermediate Level.en_US.srt 11.3 KB
  22. Confidence Intervals (pt 3) - Intermediate Level.mp4 21.5 MB
  23. Confidence Intervals (pt 4) - Intermediate Level.en_US.srt 12.1 KB
  23. Confidence Intervals (pt 4) - Intermediate Level.mp4 25.8 MB
  24. Confidence Intervals (pt 5) - Intermediate Level.en_US.srt 10.8 KB
  24. Confidence Intervals (pt 5) - Intermediate Level.mp4 22.8 MB
  25. Colab Notebook.url 102.4 B
  25. Confidence Intervals Code.en_US.srt 6.5 KB
  25. Confidence Intervals Code.mp4 28.8 MB
  26. Hypothesis Testing - Examples.en_US.srt 8.4 KB
  26. Hypothesis Testing - Examples.mp4 18.4 MB
  27. Statistical Significance.en_US.srt 6.2 KB
  27. Statistical Significance.mp4 12.9 MB
  28. Hypothesis Testing - The API Approach.en_US.srt 10.3 KB
  28. Hypothesis Testing - The API Approach.mp4 26 MB
  29. Hypothesis Testing - Accept Or Reject.en_US.srt 2.6 KB
  29. Hypothesis Testing - Accept Or Reject.mp4 6.9 MB
  30. Hypothesis Testing - Further Examples.en_US.srt 5.7 KB
  30. Hypothesis Testing - Further Examples.mp4 13.6 MB
  31. Z-Test Theory (pt 1).en_US.srt 9.2 KB
  31. Z-Test Theory (pt 1).mp4 20.8 MB
  32. Z-Test Theory (pt 2).en_US.srt 9 KB
  32. Z-Test Theory (pt 2).mp4 16.9 MB
  33. Colab Notebook.url 102.4 B
  33. Z-Test Code (pt 1).en_US.srt 13.9 KB
  33. Z-Test Code (pt 1).mp4 84 MB
  34. Z-Test Code (pt 2).en_US.srt 5.7 KB
  34. Z-Test Code (pt 2).mp4 34.2 MB
  35. A B Test Exercise.en_US.srt 4.5 KB
  35. A B Test Exercise.mp4 8.6 MB
  36. Classical A B Testing Section Summary.en_US.srt 11.5 KB
  36. Classical A B Testing Section Summary.mp4 25.4 MB
  5 - Bayesian A B Testing
  37. Section Introduction The Explore-Exploit Dilemma.en_US.srt 11.9 KB
  37. Section Introduction The Explore-Exploit Dilemma.mp4 28.1 MB
  38. Applications of the Explore-Exploit Dilemma.en_US.srt 9.3 KB
  38. Applications of the Explore-Exploit Dilemma.mp4 25.6 MB
  39. Epsilon-Greedy Theory.en_US.srt 8.3 KB
  39. Epsilon-Greedy Theory.mp4 16.3 MB
  40. Calculating a Sample Mean (pt 1).en_US.srt 6.7 KB
  40. Calculating a Sample Mean (pt 1).mp4 12.9 MB
  41. Epsilon-Greedy Beginner's Exercise Prompt.en_US.srt 5.7 KB
  41. Epsilon-Greedy Beginner's Exercise Prompt.mp4 20.4 MB
  42. Designing Your Bandit Program.en_US.srt 4.9 KB
  42. Designing Your Bandit Program.mp4 16.4 MB
  43. Epsilon-Greedy in Code.en_US.srt 7.5 KB
  43. Epsilon-Greedy in Code.mp4 30.7 MB
  44. Comparing Different Epsilons.en_US.srt 5.9 KB
  44. Comparing Different Epsilons.mp4 29.9 MB
  45. Optimistic Initial Values Theory.en_US.srt 6.4 KB
  45. Optimistic Initial Values Theory.mp4 13.3 MB
  46. Optimistic Initial Values Beginner's Exercise Prompt.en_US.srt 2.6 KB
  46. Optimistic Initial Values Beginner's Exercise Prompt.mp4 9.5 MB
  47. Optimistic Initial Values Code.en_US.srt 4.5 KB
  47. Optimistic Initial Values Code.mp4 14.6 MB
  48. UCB1 Theory.en_US.srt 17.8 KB
  48. UCB1 Theory.mp4 32.7 MB
  49. UCB1 Beginner's Exercise Prompt.en_US.srt 2.4 KB
  49. UCB1 Beginner's Exercise Prompt.mp4 7.4 MB
  50. UCB1 Code.en_US.srt 3.4 KB
  50. UCB1 Code.mp4 14.9 MB
  51. Bayesian Bandits Thompson Sampling Theory (pt 1).en_US.srt 15.1 KB
  51. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 31.9 MB
  52. Bayesian Bandits Thompson Sampling Theory (pt 2).en_US.srt 20.7 KB
  52. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 42.8 MB
  53. Thompson Sampling Beginner's Exercise Prompt.en_US.srt 3.1 KB
  53. Thompson Sampling Beginner's Exercise Prompt.mp4 15.7 MB
  54. Thompson Sampling Code.en_US.srt 5 KB
  54. Thompson Sampling Code.mp4 25.8 MB
  55. Thompson Sampling With Gaussian Reward Theory.en_US.srt 13.3 KB
  55. Thompson Sampling With Gaussian Reward Theory.mp4 54.1 MB
  56. Thompson Sampling With Gaussian Reward Code.en_US.srt 6.5 KB
  56. Thompson Sampling With Gaussian Reward Code.mp4 35.2 MB
  57. Exercise on Gaussian Rewards.en_US.srt 1.4 KB
  57. Exercise on Gaussian Rewards.mp4 3.9 MB
  58. Why don't we just use a library.en_US.srt 6.8 KB
  58. Why don't we just use a library.mp4 15.6 MB
  59. Nonstationary Bandits.en_US.srt 8.6 KB
  59. Nonstationary Bandits.mp4 17.8 MB
  6 - Bayesian A B Testing Extension
  62. More about the Explore-Exploit Dilemma.en_US.srt 8.8 KB
  62. More about the Explore-Exploit Dilemma.mp4 25.3 MB
  63. Confidence Interval Approximation vs. Beta Posterior.en_US.srt 4.3 KB
  63. Confidence Interval Approximation vs. Beta Posterior.mp4 17.5 MB
  64. Adaptive Ad Server Exercise.en_US.srt 6.6 KB
  64. Adaptive Ad Server Exercise.mp4 11.1 MB
  7 - Practice Makes Perfect
  1. Bayes Rule & Probability Review Quiz.html 19.5 KB
  2. Traditional A B Testing Quiz.html 19.9 KB
  3. Adaptive Methods Quiz.html 22.5 KB
  65. Intro to Exercises on Conjugate Priors.en_US.srt 6.9 KB
  65. Intro to Exercises on Conjugate Priors.mp4 16.9 MB
  66. Exercise Die Roll.en_US.srt 2.9 KB
  66. Exercise Die Roll.mp4 6 MB
  67. The most important quiz of all - Obtaining an infinite amount of practice.en_US.srt 10.9 KB
  67. The most important quiz of all - Obtaining an infinite amount of practice.mp4 52.5 MB
  8 - Appendix FAQ Intro
  68. What is the Appendix.en_US.srt 5.3 KB
  68. What is the Appendix.mp4 15.5 MB
  9 - Setting Up Your Environment (FAQ by Student Request)
  69. Pre-Installation Check.en_US.srt 5.7 KB
  69. Pre-Installation Check.mp4 13.1 MB
  70. Anaconda Environment Setup.en_US.srt 17.7 KB
  70. Anaconda Environment Setup.mp4 309.1 MB
  71. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.en_US.srt 13.4 KB
  71. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 192.3 MB
  60. Bandit Summary, Real Data, and Online Learning.en_US.srt 7.5 KB
  60. Bandit Summary, Real Data, and Online Learning.mp4 17.1 MB
  61. (Optional) Alternative Bandit Designs.en_US.srt 12.9 KB
  61. (Optional) Alternative Bandit Designs.mp4 34.1 MB
  6. Review Section Introduction.en_US.srt 1.5 KB
  6. Review Section Introduction.mp4 3.5 MB
  7. Probability and Bayes' Rule Review.en_US.srt 5.8 KB
  7. Probability and Bayes' Rule Review.mp4 11.8 MB
  8. Calculating Probabilities - Practice.en_US.srt 11.5 KB
  8. Calculating Probabilities - Practice.mp4 28.1 MB
  9. The Gambler.en_US.srt 6.5 KB
  9. The Gambler.mp4 15 MB
  4. Real-World Examples of A B Testing.en_US.srt 8.4 KB
  4. Real-World Examples of A B Testing.mp4 20 MB
  5. What is Bayesian Machine Learning.en_US.srt 13.9 KB
  5. What is Bayesian Machine Learning.mp4 30.7 MB
  80. BONUS.en_US.srt 7.8 KB
  80. BONUS.mp4 58.4 MB
  76. How to Succeed in this Course (Long Version).en_US.srt 13.1 KB
  76. How to Succeed in this Course (Long Version).mp4 23.8 MB
  77. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.en_US.srt 28.3 KB
  77. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 60.5 MB
  78. Machine Learning and AI Prerequisite Roadmap (pt 1).en_US.srt 14.6 KB
  78. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 118.8 MB
  79. Machine Learning and AI Prerequisite Roadmap (pt 2).en_US.srt 20.9 KB
  79. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 136.6 MB
  72. How to Code by Yourself (part 1).en_US.srt 20 KB
  72. How to Code by Yourself (part 1).mp4 93.7 MB
  73. How to Code by Yourself (part 2).en_US.srt 11.7 KB
  73. How to Code by Yourself (part 2).mp4 29.4 MB
  74. Proof that using Jupyter Notebook is the same as not using it.en_US.srt 12.7 KB
  74. Proof that using Jupyter Notebook is the same as not using it.mp4 108.7 MB
  75. Python 2 vs Python 3.en_US.srt 5.5 KB
  75. Python 2 vs Python 3.mp4 10.5 MB
  2. Github Link.url 102.4 B
  2. Where to get the code for this course.en_US.srt 14.9 KB
  2. Where to get the code for this course.mp4 75.1 MB
  3. How to succeed in this course.en_US.srt 3.9 KB
  3. How to succeed in this course.mp4 9 MB

Description


Bayesian Machine Learning in Python: A/B Testing
https://WebToolTip.com
Last updated 2/2026

Created by Lazy Programmer Inc., Lazy Programmer Team

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 80 Lectures ( 10h 26m ) | Size: 2.62 GB
Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
What you'll learn

⚡ Use adaptive algorithms to improve A/B testing performance

⚡ Understand the difference between Bayesian and frequentist statistics

⚡ Apply Bayesian methods to A/B testing
Requirements

❗ Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)

❗ Python coding with the Numpy stack

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