AI

A practical guide to turning raw, messy data into the clean numeric inputs a model can actually learn from, covering encoding, scaling, feature creation, missing data, and the selection tradeoffs that separate a model that generalizes from one that just memorizes. This is part 3 of the Applied Machine Learning track, and it assumes you're comfortable with the basics of supervised and unsupervised learning from the first two courses. Built for anyone who's trained a model on a clean textbook dataset and now needs to handle the real thing.

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