# patrickloeber/ml-study-plan

The Ultimate FREE Machine Learning Study Plan

Repository: https://github.com/patrickloeber/ml-study-plan
Canonical: https://ross.abutalabs.com/products/ml-study-plan
License Family: other
Last push: 2024-06-11T08:39:38+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2358, "days_push": 813, "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 3222, forks 426 (observed 2026-08-28T04:07:49.806572+00:00)

## What it is
A curated, free machine learning study plan with links to courses covering math prerequisites, Python, ML theory, and practical projects. It is a learning roadmap document rather than software.

## Use cases
- learn machine learning from scratch
- find free ML courses
- become a machine learning engineer
- plan a self-study ML curriculum
- prepare for Kaggle competitions
- review math prerequisites for machine learning

## When to choose
- you want a structured, free path to learn ML
- you need curated course recommendations without sponsorships
- you are self-studying and want theory plus project guidance

## When to avoid
- you need ML software, libraries, or code
- you want a paid, mentor-led bootcamp
- you need a quick crash course rather than a long-term plan

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: study-plan, free-resources, curriculum, self-learning, kaggle

## Member repositories
- patrickloeber/ml-study-plan (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:49.806572+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 2026-08-29T18:44:19.806207+00:00, confidence not recorded.
  - readme: https://github.com/patrickloeber/ml-study-plan (fetched 2026-08-28T04:07:49.806572+00:00, sha ffcd97151493)
- Data as of 2026-08-30T08:39:29.467469+00:00.