This short course helps you validate and explain machine learning models with confidence. You’ll learn practical strategies for using k-fold cross-validation and stratified sampling to estimate performance more accurately, especially when working with imbalanced data. You’ll also explore feature-importance techniques, including SHAP, to understand how your model behaves and how to explain its decisions clearly to technical and non-technical audiences.
Through accessible videos, short readings, and hands-on activities, you’ll strengthen your ability to evaluate models beyond a single accuracy score. By the end of the course, you’ll know how to choose the right validation strategy, interpret model explanations, and communicate insights that support responsible deployment in real-world domains like fraud detection and loan approvals.
