This course develops the core discipline of predictive machine learning. Learners frame business and scientific problems, prepare reliable datasets, build regression and classification models, engineer features, compare statistical and machine-learning approaches, and evaluate generalization. The course emphasizes leakage prevention, imbalance handling, calibration, uncertainty, bias-variance diagnosis, error analysis, and reliable model handoff.