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amueller/scipy_2015_sklearn_tutorial
Scikit-Learn tutorial material for Scipy 2015
Tutorial materials from Andreas Mueller's (scikit-learn core dev) SciPy 2015 workshop, covering the full sklearn API from basics through pipelines, SVMs, and out-of-core learning. It's a snapshot of how sklearn was taught in 2015 — structured, well-sequenced, and built by someone who actually wrote the library. Useful as a reference for people learning the fundamentals, not for anyone doing current work.
The sequencing is genuinely good — it builds from data representation to the estimator interface to model selection, which is the right order to teach this material. Having solutions alongside the exercises is practical rather than just showing the happy path. Mueller is a scikit-learn maintainer so the API usage is idiomatic. The video recordings make this a complete course, not just notebook dumps.
Last pushed in September 2015 — this is a decade-old artifact. Syntax and APIs have drifted; anything touching GridSearchCV, Pipeline, or preprocessing will look slightly off against current sklearn. Uses ipython notebook commands pre-Jupyter rename. No neural networks or gradient boosting (XGBoost, LightGBM), which weren't sklearn staples then but are table stakes now. If you're learning sklearn in 2026, the official user guide and newer tutorials are strictly better.