Machine Learning Foundations

OBJECTIVE: understand what machine learning is and set up the two ML projects (German Credit and Electricity Demand) properly — data, synthetic data, splits and baselines. WHAT WE COVER: • ML workspace and an experiment leaderboard • Rules vs learning; types of ML and problem types; overfitting; when ML is not the answer • From business problem to ML project; data sources and labels • Data synthesis: when to use it, 3 methods and quality checks • X/y, three-way split, leakage-safe pipeline, scaling and baselines AFTER THIS MODULE YOU CAN: • Frame an ML problem and prepare model-ready data without leakage • Generate and check synthetic data • Set the baselines every model must beat