Machine Learning with Python: Build, Evaluate and Deploy Models

Take a business problem from raw data to a deployed, monitored model with scikit-learn. Frame the problem, prepare and engineer features without leaking the test set, train and compare regression, classification and clustering models, and evaluate them with the metric the business actually needs. Then tune with cross-validation, track every run in MLflow, save models safely with skops and serve one behind a FastAPI endpoint with a plan for drift and retraining. Follow an insurer’s claims review model from question to service, and build it yourself in a free lab that runs entirely on your own computer.