A/B Testing and Online Experimentation for Machine Learning

Deploying a machine learning model is only the first step; understanding its real-world impact on user behavior requires rigorous statistical validation. This written course guides you through the foundational concepts of online experimentation, helping you transition from offline evaluation metrics to live production testing. What you will learn: 1. Understand core A/B testing principles, hypothesis formulation, and statistical significance. 2. Design robust experiments specifically tailored for evaluating machine learning model updates. 3. Detect common pitfalls like Sample Ratio Mismatch (SRM) and carryover effects in live environments. 4. Analyze experimental data using statistical methods to measure feature impact accurately. 5. Explore modern experimentation strategies, including multi-armed bandits for dynamic traffic routing. Our curriculum begins with key terminology, basic statistical definitions, and foundational concepts before moving into practical experimentation design and analysis. This course is designed for beginner data scientists, machine learning enthusiasts, and product analysts with no prior experimentation experience. Start reading today to make data-driven decisions for your machine learning systems.