Practical Artificial Intelligence for A/B Testing (Udemy.com)

Apply AI algorithms to leverage the power of A/B tests experiments

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Created by: Packt Publishing

Produced in 2021

What you will learn

Deploy an AI agent to perform A/B test between two versions of a simple webpage.
Explore Reinforcement Learning topics such as agent, environment, actions, and rewards.
Discover how to solve Multi-Armed bandit problem and use it for A/B test.
Apply Reinforcement Learning in a real-world use case besides the traditional games examples.
Clarify once for all the difference between Exploration and Exploitation in an AI context.
Design, code and experiment different strategies for AI agent in Python.
Deploy the agent to perform a real A/B test using Flask, a web framework.

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Course Description

A/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.
About the Author
Meigarom Diego Fernandes Lopes is a Senior Data Scientist that has been working on data projects for 4 years. He is an expert in deploy Machine Learning models to solve business problems and support decision-makers with cut-edge technologies. His primary interests are in Reinforcement Learning, Recommendation Systems, Machine learning models in general and data engineering process. He has worked on game-changing projects like NLP algorithms to measure satisfaction of customer about the service through comments of the E-hailing company, recommendation system able to suggest financial products to invest money and he is currently working on an fashion e-commerce executing projects like fashion model image classification, product auto-tagging, and sales forecast. Finally, he writes about Machine learning models on his own Medium blog.Who this course is for:

This course is for Python developers who are looking for different approaches to perform A/B tests to leverage their results.

Time: 3h

Instructor Details

Packt Publishing

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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
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