Ml Model Training

Handles the full ML training pipeline from data prep through evaluation with scikit-learn, PyTorch, and TensorFlow. The examples cover the essentials like proper train/val/test splits, feature scaling, and regularization techniques. What's actually useful here is the "Known Issues Prevention" section that walks through common mistakes like data leakage and ignoring class imbalance with concrete before/after code. The reference files go deeper on framework-specific patterns like PyTorch training loops with early stopping and Keras callbacks. Good for spinning up a model quickly or debugging why your validation metrics look suspicious.