Machine Learning

Supervised Learning

Learning a mapping from inputs to known answers. Most production ML is still this.

You have examples with the right answer attached, and you fit a function that reproduces those answers well enough to generalise to new cases. Classification predicts a category; regression predicts a number.

Almost all the difficulty is in the setup rather than the algorithm: getting honest labels, splitting data so the test set genuinely represents the future, and noticing when a feature is quietly leaking the answer.

Also in Machine Learning

Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression PCA

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