2. Experiments & Randomization in Machine Learning

About this lesson

In this video, we break down the foundational concepts of experimental design for Data Scientists and ML Engineers. We define what exactly constitutes an "experiment" (vs observational study), explain the critical role of Control vs Treatment groups, and demonstrate why Randomization is the only way to establish causality. We also cover the engineering implementation of randomization using deterministic hashing algorithms to ensure user consistency. Key Concepts: - Controlled Intervention (Ceteris Paribus) - Control vs Treatment Groups - Selection Bias vs Random Assignment - Deterministic Hashing for A/B Tests

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