{"adoption": {"forks": 4373, "observed_at": "2026-08-28T04:10:59.490016+00:00", "stars": 12644}, "canonical_url": "https://ross.abutalabs.com/products/python-machine-learning-book", "card": {"archived": false, "artifact_type": "learning-resource", "description": "The \"Python Machine Learning (1st edition)\"  book code repository and info resource", "domain": ["machine-learning", "data-science", "tutorials"], "enriched": true, "function": ["machine-learning", "data-science"], "health_score": 76, "homepage": null, "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["rasbt/python-machine-learning-book"], "name": "rasbt/python-machine-learning-book", "platform": ["python"], "pushed_at": "2026-07-18T13:09:40+00:00", "repo": "rasbt/python-machine-learning-book", "stars": 12644, "tags": ["jupyter-notebooks", "scikit-learn", "neural-networks", "book-code", "supervised-learning"], "topics": ["machine-learning", "machine-learning-algorithms", "logistic-regression", "data-science", "data-mining", "python", "scikit-learn", "neural-network"], "urls": [], "use_cases": ["learn machine learning from scratch with python", "find code examples for scikit-learn algorithms", "understand neural network fundamentals with numpy", "study logistic regression and classification implementations", "get jupyter notebooks accompanying a machine learning book", "learn data preprocessing and model evaluation best practices"], "what_it_is": "The official code repository for the 1st edition of the book 'Python Machine Learning' by Sebastian Raschka, containing Jupyter Notebook code examples for each chapter. It covers machine learning theory and practice using NumPy, scikit-learn, and Theano.", "when_to_avoid": ["you want the latest edition's content - use the 2nd or 3rd edition repositories instead", "you need a production-ready ML library rather than educational code", "you rely on Theano, which is no longer maintained"], "when_to_choose": ["you are reading the 1st edition of the Python Machine Learning book and want its code", "you want well-explained notebook-style walkthroughs of classic ML algorithms", "you prefer learning theory alongside runnable NumPy/scikit-learn examples"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/python-machine-learning-book", "repo": "rasbt/python-machine-learning-book", "role": "main", "score": 65}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:10:59.490016+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:13:53.878356+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "b8a7dd409542235dbd44b1684e8a58033e8c56faa832d3219f44208cd034d00b", "fetched_at": "2026-08-28T04:10:59.490016+00:00", "kind": "readme", "missing": false, "url": "https://github.com/rasbt/python-machine-learning-book"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 93, "longevity": 100, "rhythm": 8}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 4044, "days_push": 46, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 65, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}