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amueller/ml-workshop-1-of-4
Introduction to Machine learning with Python, 4h interactive workshop
A 4-part interactive workshop series by Andreas Mueller, one of the core scikit-learn contributors and co-author of the O'Reilly scikit-learn book. Part 1 covers supervised learning basics, preprocessing, and missing value imputation using scikit-learn. Aimed at people who already know Python and pandas but haven't touched ML yet.
Comes from someone who actually built scikit-learn, so the API explanations and the why-behind-the-design are accurate and not cargo-culted. The slide deck covers information leakage in preprocessing — a trap that most intro tutorials skip entirely. Notebooks include solution files, which is genuinely useful for self-study. The check_env notebook is a small but thoughtful touch that saves the first 20 minutes of every workshop.
Last updated April 2020 — six years stale. scikit-learn has changed meaningfully since 0.22 (the HistGradientBoosting estimators alone are worth covering). The HTML slides are rendered RevealJS blobs with no clean way to contribute fixes or fork the content into your own workshop format. Only part 1 of 4 lives here; you need three more repos to get the full picture, and there's no meta-repo tying them together. For self-study without an instructor the slides don't stand alone — they lean on verbal explanation.