What it does This repo holds the complete source code for Peter Harrington’s Machine Learning in Action (Manning). It’s a collection of chapter-by-chapter examples covering classic algorithms—think k-NN, decision trees, SVMs, and the like—implemented in raw Python with NumPy for the heavy lifting.

The interesting bit The README openly admits the code targets Python 2.6 and shrugs at 2.7 compatibility. The author practically invites you to fork it for Python 3 or IronPython, which in 2024 reads less like encouragement and more like a homework assignment. The repo’s longevity—2,398 stars with zero topic tags—suggests it fills a niche as a readable, no-framework reference implementation.

Key highlights

• Pure Python implementations of foundational ML algorithms (no scikit-learn abstraction layers)
• NumPy is the only external dependency for most examples
• Direct tie to a specific Manning textbook with forum support linked
• Explicitly not maintained for modern Python; community forks expected to handle migration
• HTML listed as primary language (likely from bundled HTML documentation or index files)

Caveats

• Python 2.6/2.7 only; running on 3.x requires unspecified manual fixes
• No test suite, CI, or modern packaging visible in the README
• “If you have trouble… let us know” points to a Manning forum, not GitHub issues

Verdict Grab this if you want to see algorithms unwrapped from library black boxes, or if you’re following Harrington’s book chapter-for-chapter. Skip it if you need production-ready, maintained code—scikit-learn’s docs are more current and actually run without a time machine.