patrickloeber/ml-study-plan resource
The Ultimate FREE Machine Learning Study Plan observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2358
- days_rel: n/a
- days_push: 813
- n_releases_24m: 0
Adoption not part of the score
3222 stars · 426 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
learning-resource · maturity active
machine-learning developer-tools machine-learning tutorials education cross-platform study-plan free-resources curriculum self-learning kaggle
1 source
- readme: https://github.com/patrickloeber/ml-study-plan · fetched 2026-08-28 · ffcd97151493
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| patrickloeber/ml-study-plan | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="patrickloeber/ml-study-plan")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem