Hands-on Approaches to Handling Data Imbalance

In this course, you'll learn to recognize and address class imbalance in datasets. Explore practical undersampling and oversampling techniques, visualize their effects, and apply advanced resampling strategies. By the end, you'll be able to train models that perform better on imbalanced data.

This course focuses on evaluating models with imbalanced data, and explores advanced techniques including cost-sensitive learning, ensemble methods, and anomaly detection for extreme imbalance. You'll learn appropriate evaluation strategies specifically designed for imbalanced datasets, helping you choose the right metrics and avoid common pitfalls.