Machine Learning
Learning patterns from data: training, validation, overfitting.
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This course has 3 lessons. Start with the first one and use the next / previous links at the bottom of each lesson — the sidebar keeps the whole course in order.
Lessons
- Machine learning in one pageSupervised, unsupervised and reinforcement learning, what features and labels are, and how to tell whether learning happened at all.
- Evaluation and overfittingCross-validation, the precision/recall trade-off, and how to spot a model that memorised the training set.
- Pipelines and saving modelsChaining preprocessing with a model so nothing leaks, and persisting the whole pipeline for consistent inference.
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Last refreshed 2026-09-17.