# josephmisiti/awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

Repository: https://github.com/josephmisiti/awesome-machine-learning
Canonical: https://ross.abutalabs.com/products/awesome-machine-learning
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-08-26T21:09:24+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 4432, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 74172, forks 15630 (observed 2026-08-28T04:12:21.401699+00:00)

## What it is
A curated awesome-list of machine learning frameworks, libraries, and software organized by programming language. It also links to companion lists of free ML books, courses, blogs, and events.

## Use cases
- find machine learning libraries for a specific language
- discover ML frameworks and tools
- find free machine learning courses and books
- explore data science resources
- compare ML libraries across ecosystems

## When to choose
- you want a broad, community-curated directory of ML tools
- you're exploring which ML library to adopt in a given language
- you need pointers to ML learning materials like books and courses

## When to avoid
- you need detailed documentation or tutorials rather than links
- you want an actively maintained software tool rather than a list
- you need curated coverage of a narrow ML subfield

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, data-science, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, curated-list, machine-learning-resources, reference

## Member repositories
- josephmisiti/awesome-machine-learning (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:21.401699+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:14:59.252567+00:00, confidence not recorded.
  - readme: https://github.com/josephmisiti/awesome-machine-learning (fetched 2026-08-28T04:12:21.401699+00:00, sha 6871babd68d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.