# dive-into-machine-learning/dive-into-machine-learning

Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)

Repository: https://github.com/dive-into-machine-learning/dive-into-machine-learning
Canonical: https://ross.abutalabs.com/products/dive-into-machine-learning
Homepage: http://hangtwenty.github.io/dive-into-machine-learning/
License: CC-BY-4.0
License Family: other
Archived: true
Last push: 2022-06-17T23:22:08+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4210, "days_push": 1538, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11390, forks 1866 (observed 2026-08-28T04:10:46.916735+00:00)

## 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.

## 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

## 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

## 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

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, tutorials, data-science, education
- platform: python, cross-platform
- tags: awesome-list, jupyter-notebook, curated-links, free-course, self-learning

## Member repositories
- dive-into-machine-learning/dive-into-machine-learning (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.916735+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 2026-08-29T17:15:38.075778+00:00, confidence not recorded.
  - readme: https://github.com/dive-into-machine-learning/dive-into-machine-learning (fetched 2026-08-28T04:10:46.916735+00:00, sha d098e18f5e21)
- Data as of 2026-08-30T08:39:29.467469+00:00.