Bayesian Machine Learning in Python- AB Testing

Bayesian Machine Learning in Python: A/B Testing, available at $99.99, has an average rating of 4.67, with 114 lectures, based on 7173 reviews, and has 40622 subscribers.

You will learn about Use adaptive algorithms to improve A/B testing performance Understand the difference between Bayesian and frequentist statistics Apply Bayesian methods to A/B testing This course is ideal for individuals who are Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work It is particularly useful for Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work.

Enroll now: Bayesian Machine Learning in Python: A/B Testing

Summary

Title: Bayesian Machine Learning in Python: A/B Testing

Price: $99.99

Average Rating: 4.67

Number of Lectures: 114

Number of Published Lectures: 80

Number of Curriculum Items: 114

Number of Published Curriculum Objects: 80

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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)

UNIQUE FEATURES

Every line of code explained in detail – email me any time if you disagree

No 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 scratch

Not afraid of university-level math – get important details about algorithms that other courses leave out

Course Curriculum

Chapter 1: Introduction and Outline

Lecture 1: Whats this course all about?

Lecture 2: Where to get the code for this course

Lecture 3: How to succeed in this course

Chapter 2: The High-Level Picture

Lecture 1: Real-World Examples of A/B Testing

Lecture 2: What is Bayesian Machine Learning?

Chapter 3: Bayes Rule and Probability Review

Lecture 1: Review Section Introduction

Lecture 2: Probability and Bayes Rule Review

Lecture 3: Calculating Probabilities – Practice

Lecture 4: The Gambler

Lecture 5: The Monty Hall Problem

Lecture 6: Maximum Likelihood Estimation – Bernoulli

Lecture 7: Click-Through Rates (CTR)

Lecture 8: Maximum Likelihood Estimation – Gaussian (pt 1)

Lecture 9: Maximum Likelihood Estimation – Gaussian (pt 2)

Lecture 10: CDFs and Percentiles

Lecture 11: Probability Review in Code

Lecture 12: Probability Review Section Summary

Lecture 13: Beginners: Fix Your Understanding of Statistics vs Machine Learning

Lecture 14: Suggestion Box

Chapter 4: Traditional A/B Testing

Lecture 1: Confidence Intervals (pt 1) – Intuition

Lecture 2: Confidence Intervals (pt 2) – Beginner Level

Lecture 3: Confidence Intervals (pt 3) – Intermediate Level

Lecture 4: Confidence Intervals (pt 4) – Intermediate Level

Lecture 5: Confidence Intervals (pt 5) – Intermediate Level

Lecture 6: Confidence Intervals Code

Lecture 7: Hypothesis Testing – Examples

Lecture 8: Statistical Significance

Lecture 9: Hypothesis Testing – The API Approach

Lecture 10: Hypothesis Testing – Accept Or Reject?

Lecture 11: Hypothesis Testing – Further Examples

Lecture 12: Z-Test Theory (pt 1)

Lecture 13: Z-Test Theory (pt 2)

Lecture 14: Z-Test Code (pt 1)

Lecture 15: Z-Test Code (pt 2)

Lecture 16: A/B Test Exercise

Lecture 17: Classical A/B Testing Section Summary

Chapter 5: Bayesian A/B Testing

Lecture 1: Section Introduction: The Explore-Exploit Dilemma

Lecture 2: Applications of the Explore-Exploit Dilemma

Lecture 3: Epsilon-Greedy Theory

Lecture 4: Calculating a Sample Mean (pt 1)

Lecture 5: Epsilon-Greedy Beginners Exercise Prompt

Lecture 6: Designing Your Bandit Program

Lecture 7: Epsilon-Greedy in Code

Lecture 8: Comparing Different Epsilons

Lecture 9: Optimistic Initial Values Theory

Lecture 10: Optimistic Initial Values Beginners Exercise Prompt

Lecture 11: Optimistic Initial Values Code

Lecture 12: UCB1 Theory

Lecture 13: UCB1 Beginners Exercise Prompt

Lecture 14: UCB1 Code

Lecture 15: Bayesian Bandits / Thompson Sampling Theory (pt 1)

Lecture 16: Bayesian Bandits / Thompson Sampling Theory (pt 2)

Lecture 17: Thompson Sampling Beginners Exercise Prompt

Lecture 18: Thompson Sampling Code

Lecture 19: Thompson Sampling With Gaussian Reward Theory

Lecture 20: Thompson Sampling With Gaussian Reward Code

Lecture 21: Exercise on Gaussian Rewards

Lecture 22: Why dont we just use a library?

Lecture 23: Nonstationary Bandits

Lecture 24: Bandit Summary, Real Data, and Online Learning

Lecture 25: (Optional) Alternative Bandit Designs

Chapter 6: Bayesian A/B Testing Extension

Lecture 1: More about the Explore-Exploit Dilemma

Lecture 2: Confidence Interval Approximation vs. Beta Posterior

Lecture 3: Adaptive Ad Server Exercise

Chapter 7: Practice Makes Perfect

Lecture 1: Intro to Exercises on Conjugate Priors

Lecture 2: Exercise: Die Roll

Lecture 3: The most important quiz of all – Obtaining an infinite amount of practice

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

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

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

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

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

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 11: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 68 votes

2 stars: 100 votes

3 stars: 320 votes

4 stars: 2521 votes

5 stars: 4164 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!