by Meigarom Diego Fernandes Lopes | Released February 2019 | ISBN: 9781788990745

Apply AI algorithms to leverage the power of A/B tests experiments About This VideoImprove the outcome of your A/B test, using clear and simple Python implementations of the concepts of AI and Reinforcement Learning.Learn how to try out different solutions to solve the Multi-Armed bandit problem which is one of the most import challenges of Reinforcement Learning.The course is focused on the resolution of the challenge. Each step is composed of two portions. The first one is a brief and essential theoretical context and the second is the practical development of the solution. In DetailA/B testing is a well-known technique in web designing where designers apply it to test out different versions of the same webpage. The drawback to this technique is the waiting time to choose the best version and you lose the current performance of the webpage. To counter these drawbacks, you will learn how to build an AI Agent to A/B test the webpage in a much quicker pace using Reinforcement Learning.This course will teach you how to build and deploy an AI Agent to test multiple versions of the web page and choose the best one much faster than the traditional A/B testing method. This quick decision-making will ensure good performance of your web-page even during the experiment. By the end of this course, you will be able to deploy an AI Agent to perform an A/B test with many different strategies and to select the one which boosts its performance.The code bundle for this video course is available at- https://github.com/PacktPublishing/Practical-Artificial-Intelligence-for-A-B-Testing-Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Welcome to the World of AI and A/B Testing
   • Course Overview 00:04:30
   • A/B Testing - A Quick Overview 00:02:42
   • Setting Up the Python Environment 00:03:45
2. Chapter 2 : Development of Web Elements to Simulate A/B Testing
   • Creating Web Pages in HTML 00:11:55
   • Creating a Web Application with Flask 00:05:10
   • Developing a Web Scraper in Python 00:06:41
3. Chapter 3 : Your First Agent: The Omniscient Agent
   • The Multi-Armed Bandit Problem 00:03:24
   • Implementing the Omniscient Agent in Python 00:14:47
4. Chapter 4 : Deployment of the Omniscient Agent
   • Problem Solving with A/B Testing 00:03:02
   • Integrate the Agent into Flask App 00:11:27
   • Code the Omniscient Agent Class 00:12:17
   • Simulate the A/B Testing 00:09:58
5. Chapter 5 : Random Agent: Pulling the Lever by Chance
   • Drawbacks of the Omniscient Agent 00:03:04
   • Implement the Random Agent in Python 00:12:16
6. Chapter 6 : Exploration versus Exploitation Dilemma
   • Exploration versus Exploitation Basics 00:08:22
   • Use Cases of AI A/B Testing 00:12:00
7. Chapter 7 : A Greedy Agent - Pull Only the Winner
   • Drawbacks of the Random Agent 00:04:16
   • Implement the Greedy Agent in Python 00:06:16
8. Chapter 8 : Good Balance Strategy: Epsilon-Greedy Agent
   • Drawbacks of the Greedy Agent 00:05:49
   • Implement the Epsilon-Greedy Agent in Python 00:08:45
9. Chapter 9 : The Best AI Agent: The Thompson Agent
   • Drawbacks of the Epsilon-Greedy Agent 00:09:23
   • Implement the Thompson Agent in Python 00:08:38
10. Chapter 10 : Deploy the Thompson Agent to Perform the A/B Testing
    • Working of the Agent in a Production Environment 00:02:49
    • Integrate the Thompson Agent into the Flask App 00:08:37
    • Simulate the AI A/B Testing 00:03:43
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