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Development Data Science A/B Testing

Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
Bestseller
Rating: 4.6 out of 54.6 (4,354 ratings)
24,429 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

11 sections • 78 lectures • 10h 15m total length

  • Preview03:55
  • Where to get the code for this course
    05:01
  • How to succeed in this course
    05:51

  • Real-World Examples of A/B Testing
    06:46
  • What is Bayesian Machine Learning?
    11:33

  • Review Section Introduction
    01:22
  • Probability and Bayes' Rule Review
    05:27
  • Calculating Probabilities - Practice
    10:25
  • The Gambler
    05:42
  • The Monty Hall Problem
    07:01
  • Maximum Likelihood Estimation - Bernoulli
    11:42
  • Click-Through Rates (CTR)
    02:08
  • Maximum Likelihood Estimation - Gaussian (pt 1)
    10:07
  • Maximum Likelihood Estimation - Gaussian (pt 2)
    08:40
  • CDFs and Percentiles
    09:38
  • Probability Review in Code
    10:24
  • Probability Review Section Summary
    05:12
  • Beginners: Fix Your Understanding of Statistics vs Machine Learning
    06:47
  • Suggestion Box
    03:03

  • Confidence Intervals (pt 1) - Intuition
    05:09
  • Confidence Intervals (pt 2) - Beginner Level
    04:45
  • Confidence Intervals (pt 3) - Intermediate Level
    10:25
  • Confidence Intervals (pt 4) - Intermediate Level
    11:42
  • Confidence Intervals (pt 5) - Intermediate Level
    10:08
  • Confidence Intervals Code
    06:32
  • Hypothesis Testing - Examples
    07:15
  • Statistical Significance
    05:26
  • Hypothesis Testing - The API Approach
    09:17
  • Hypothesis Testing - Accept Or Reject?
    02:23
  • Hypothesis Testing - Further Examples
    04:59
  • Z-Test Theory (pt 1)
    08:47
  • Z-Test Theory (pt 2)
    08:30
  • Z-Test Code (pt 1)
    13:02
  • Z-Test Code (pt 2)
    05:54
  • A/B Test Exercise
    03:54
  • Classical A/B Testing Section Summary
    09:57

  • Section Introduction: The Explore-Exploit Dilemma
    10:17
  • Applications of the Explore-Exploit Dilemma
    07:49
  • Preview07:04
  • Calculating a Sample Mean (pt 1)
    05:56
  • Epsilon-Greedy Beginner's Exercise Prompt
    05:05
  • Designing Your Bandit Program
    04:09
  • Epsilon-Greedy in Code
    07:12
  • Comparing Different Epsilons
    06:02
  • Optimistic Initial Values Theory
    05:40
  • Optimistic Initial Values Beginner's Exercise Prompt
    02:26
  • Optimistic Initial Values Code
    04:18
  • UCB1 Theory
    14:32
  • UCB1 Beginner's Exercise Prompt
    02:14
  • UCB1 Code
    03:28
  • Bayesian Bandits / Thompson Sampling Theory (pt 1)
    12:43
  • Bayesian Bandits / Thompson Sampling Theory (pt 2)
    17:35
  • Thompson Sampling Beginner's Exercise Prompt
    02:50
  • Thompson Sampling Code
    05:03
  • Thompson Sampling With Gaussian Reward Theory
    11:24
  • Thompson Sampling With Gaussian Reward Code
    06:18
  • Why don't we just use a library?
    05:40
  • Nonstationary Bandits
    07:11
  • Bandit Summary, Real Data, and Online Learning
    06:10
  • (Optional) Alternative Bandit Designs
    10:05

  • More about the Explore-Exploit Dilemma
    07:38
  • Confidence Interval Approximation vs. Beta Posterior
    05:41
  • Adaptive Ad Server Exercise
    05:38

  • Intro to Exercises on Conjugate Priors
    06:04
  • Exercise: Die Roll
    02:38
  • The most important quiz of all - Obtaining an infinite amount of practice
    09:26

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38

  • How to Succeed in this Course (Long Version)
    10:24
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    22:04
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
    11:18
  • Machine Learning and AI Prerequisite Roadmap (pt 2)
    16:07

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 scratch

  • Other 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, sets

  • Numpy, Scipy, Matplotlib


WHAT 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)

Who this course is for:

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

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,419 Reviews
  • 423,004 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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