Topic overview
Learn supervised and unsupervised learning, feature engineering, validation, metrics, regularization, and deployment concerns.
Core concepts
Bias-variance, leakage, train-test splits, cross-validation, calibration, drift, model selection, and error analysis.
Why it matters
Useful ML requires more than algorithms: it needs problem framing, data quality, evaluation, and operational feedback.
Interview relevance
Expect questions about choosing models, measuring quality, diagnosing failures, and communicating tradeoffs.