Bayesian Machine Learning In Python: A/B Testing (updated 11/2022)

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

Description

This course is all about A/B testing.A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.A/B testing is all about comparing things.If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.Finally, we’ll improve on both of those by using a fully Bayesian approach.Why is the Bayesian method interesting to us in machine learning?It’s an entirely different way of thinking about probability.It’s a paradigm shift.You’ll probably need to come back to this course several times before it fully sinks in.It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.See you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…Suggested Prerequisites:Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out

Overview

Section 1: Introduction and Outline

Lecture 1 What's this course all about?

Lecture 2 Where to get the code for this course

Lecture 3 How to succeed in this course

Section 2: The High-Level Picture

Lecture 4 Real-World Examples of A/B Testing

Lecture 5 What is Bayesian Machine Learning?

Section 3: Bayes Rule and Probability Review

Lecture 6 Review Section Introduction

Lecture 7 Probability and Bayes' Rule Review

Lecture 8 Calculating Probabilities - Practice

Lecture 9 The Gambler

Lecture 10 The Monty Hall Problem

Lecture 11 Maximum Likelihood Estimation - Bernoulli

Lecture 12 Click-Through Rates (CTR)

Lecture 13 Maximum Likelihood Estimation - Gaussian (pt 1)

Lecture 14 Maximum Likelihood Estimation - Gaussian (pt 2)

Lecture 15 CDFs and Percentiles

Lecture 16 Probability Review in Code

Lecture 17 Probability Review Section Summary

Lecture 18 Beginners: Fix Your Understanding of Statistics vs Machine Learning

Lecture 19 Suggestion Box

Section 4: Traditional A/B Testing

Lecture 20 Confidence Intervals (pt 1) - Intuition

Lecture 21 Confidence Intervals (pt 2) - Beginner Level

Lecture 22 Confidence Intervals (pt 3) - Intermediate Level

Lecture 23 Confidence Intervals (pt 4) - Intermediate Level

Lecture 24 Confidence Intervals (pt 5) - Intermediate Level

Lecture 25 Confidence Intervals Code

Lecture 26 Hypothesis Testing - Examples

Lecture 27 Statistical Significance

Lecture 28 Hypothesis Testing - The API Approach

Lecture 29 Hypothesis Testing - Accept Or Reject?

Lecture 30 Hypothesis Testing - Further Examples

Lecture 31 Z-Test Theory (pt 1)

Lecture 32 Z-Test Theory (pt 2)

Lecture 33 Z-Test Code (pt 1)

Lecture 34 Z-Test Code (pt 2)

Lecture 35 A/B Test Exercise

Lecture 36 Classical A/B Testing Section Summary

Section 5: Bayesian A/B Testing

Lecture 37 Section Introduction: The Explore-Exploit Dilemma

Lecture 38 Applications of the Explore-Exploit Dilemma

Lecture 39 Epsilon-Greedy Theory

Lecture 40 Calculating a Sample Mean (pt 1)

Lecture 41 Epsilon-Greedy Beginner's Exercise Prompt

Lecture 42 Designing Your Bandit Program

Lecture 43 Epsilon-Greedy in Code

Lecture 44 Comparing Different Epsilons

Lecture 45 Optimistic Initial Values Theory

Lecture 46 Optimistic Initial Values Beginner's Exercise Prompt

Lecture 47 Optimistic Initial Values Code

Lecture 48 UCB1 Theory

Lecture 49 UCB1 Beginner's Exercise Prompt

Lecture 50 UCB1 Code

Lecture 51 Bayesian Bandits / Thompson Sampling Theory (pt 1)

Lecture 52 Bayesian Bandits / Thompson Sampling Theory (pt 2)

Lecture 53 Thompson Sampling Beginner's Exercise Prompt

Lecture 54 Thompson Sampling Code

Lecture 55 Thompson Sampling With Gaussian Reward Theory

Lecture 56 Thompson Sampling With Gaussian Reward Code

Lecture 57 Exercise on Gaussian Rewards

Lecture 58 Why don't we just use a library?

Lecture 59 Nonstationary Bandits

Lecture 60 Bandit Summary, Real Data, and Online Learning

Lecture 61 (Optional) Alternative Bandit Designs

Section 6: Bayesian A/B Testing Extension

Lecture 62 More about the Explore-Exploit Dilemma

Lecture 63 Confidence Interval Approximation vs. Beta Posterior

Lecture 64 Adaptive Ad Server Exercise

Section 7: Practice Makes Perfect

Lecture 65 Intro to Exercises on Conjugate Priors

Lecture 66 Exercise: Die Roll

Lecture 67 The most important quiz of all - Obtaining an infinite amount of practice

Section 8: Setting Up Your Environment (FAQ by Student Request)

Lecture 68 Anaconda Environment Setup

Lecture 69 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Section 9: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 70 How to Code by Yourself (part 1)

Lecture 71 How to Code by Yourself (part 2)

Lecture 72 Proof that using Jupyter Notebook is the same as not using it

Lecture 73 Python 2 vs Python 3

Section 10: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 74 How to Succeed in this Course (Long Version)

Lecture 75 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 76 Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 77 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 11: Appendix / FAQ Finale

Lecture 78 What is the Appendix?

Lecture 79 BONUS

Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work