In this short course, you’ll learn how to train and evaluate machine learning models with confidence. You’ll explore how mini-batch training and learning-rate schedulers shape convergence, how to read loss curves and logs to diagnose issues, and how class-imbalance techniques affect F1 scores. Through hands-on PyTorch practice, you’ll train models, investigate instability, and compare weighting and SMOTE. By the end, you’ll understand how to guide models toward stable, reliable performance.