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amueller/introduction_to_ml_with_python
Notebooks and code for the book "Introduction to Machine Learning with Python"
Companion notebooks for the O'Reilly book 'Introduction to Machine Learning with Python' by scikit-learn core contributor Andreas Mueller. Covers the standard supervised/unsupervised ML workflow using scikit-learn, pandas, and matplotlib. Aimed at beginners who learn better from executable code than prose alone.
The mglearn helper library is a genuine differentiator — it exists purely to make the book's visualizations reproducible without burying students in matplotlib boilerplate. Coverage of pipelines and model evaluation (chapters 5–6) is more practical than most intro material and directly maps to real workflows. The author is a scikit-learn maintainer, so the API usage patterns here are idiomatic, not cargo-culted. Binder support means zero local setup friction for someone who just wants to run a chapter.
The repo is frozen against scikit-learn 0.20 (released 2018) and hasn't been meaningfully updated since; scikit-learn is now at 1.5+ with significant API changes, so expect deprecation warnings and broken imports on a fresh install. No deep learning content at all — this was a deliberate choice when the book was written, but it's a real gap if someone picks this up expecting anything post-2018 ML. The notebooks assume the book for narrative context; without it, several chapters are just code blocks with minimal explanation. There's no requirements.txt pinning specific versions, so environment.yml is your only safety net and it's out of date.