English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 03m | 612 MB
Apply AI algorithms to leverage the power of A/B tests experiments
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.
This course is a full hands-on tutorial. It shares only the essential theoretical concepts that will context and support the practical implementation of the solution. The essential theory is explained using examples, analogies, and figures to make it self-explanatory and as clear as possible. In the practical portion, all the codes are developed on the fly during the videos, each line is explained with minimum jargon. The main object is to make the viewers succeed and overcome obstacles in their own environments.
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.