ML Model Training

This is a straightforward reference guide that walks through the standard ML training pipeline, from data prep through deployment. It covers the usual suspects: feature engineering, hyperparameter tuning, cross-validation, plus a quick reference of common algorithms across regression, classification, clustering, and neural networks. It's more of a checklist than a deep dive, so expect high-level phases rather than implementation details. Useful if you want Claude to structure a training workflow or need a reminder of what steps you're missing, but you'll still need to know your scikit-learn or PyTorch to actually build anything. Think of it as scaffolding for ML conversations rather than a tutorial.