{"adoption": {"forks": 1866, "observed_at": "2026-08-28T04:10:46.916735+00:00", "stars": 11390}, "canonical_url": "https://ross.abutalabs.com/products/dive-into-machine-learning", "card": {"archived": true, "artifact_type": "learning-resource", "description": "Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)", "domain": ["machine-learning", "tutorials", "data-science", "education"], "enriched": true, "function": ["machine-learning", "data-science", "developer-tools"], "health_score": 10, "homepage": "http://hangtwenty.github.io/dive-into-machine-learning/", "language": null, "license": "CC-BY-4.0", "license_family": "other", "maturity": "maintenance", "member_repos": ["dive-into-machine-learning/dive-into-machine-learning"], "name": "dive-into-machine-learning/dive-into-machine-learning", "platform": ["python", "cross-platform"], "pushed_at": "2022-06-17T23:22:08+00:00", "repo": "dive-into-machine-learning/dive-into-machine-learning", "stars": 11390, "tags": ["awesome-list", "jupyter-notebook", "curated-links", "free-course", "self-learning"], "topics": [], "urls": [], "use_cases": ["learn machine learning with python for free", "find jupyter notebook tutorials for ML beginners", "self-study machine learning curriculum", "hands-on machine learning resources for python developers", "introductory ML course recommendations", "learn about machine learning ethics"], "what_it_is": "A curated guide of free resources for learning machine learning with Python and Jupyter Notebooks, including notebooks, courses, and links. First published in 2016, it emphasizes hands-on learning and ML ethics.", "when_to_avoid": ["you need structured, instructor-led training with certification", "you want deep-learning-specific or framework-specific documentation", "you need actively updated content — the guide was last released in 2022"], "when_to_choose": ["you know Python and want to learn machine learning hands-on", "you prefer free, curated learning paths over paid courses", "you want Jupyter Notebook-based tutorials", "you care about responsible/ethical ML practices"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/dive-into-machine-learning", "repo": "dive-into-machine-learning/dive-into-machine-learning", "role": "main", "score": 10}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:10:46.916735+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:15:38.075778+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d098e18f5e21f53017cf5e4a688ca8e04f9b815d5ed8ec2b4978cecf769a8d3a", "fetched_at": "2026-08-28T04:10:46.916735+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dive-into-machine-learning/dive-into-machine-learning"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases", "archived"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 4210, "days_push": 1538, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 10, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}