Hands-On Machine Learning vs 
Awesome Machine Learning
Hands-On Machine Learning vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. The notebooks of the best-selling ML book vs The reference index of ML libraries, by language.
Updated regularly · curated by olud.ai
Hands-On Machine Learning vs Awesome Machine Learning at a glance
| Spec | Hands-On Machine Learning | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Book notebooks | Curated list |
| License | Apache-2.0 | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | the classic path from scikit-learn to deep learning | finding the right library in any language |
| GitHub stars | — | 73.6k |
How Hands-On Machine Learning and Awesome Machine Learning score
| Criterion | Hands-On Machine Learning | Awesome Machine Learning |
|---|---|---|
| Popularity | n/a | 4.5 |
| Maintenance | n/a | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 3.5 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
Hands-On Machine Learning
Aurélien Géron's companion notebooks: scikit-learn for classical ML, then Keras and TensorFlow for deep learning — the reference practical ML book.
- The most widely used practical ML book
- Every chapter is a runnable notebook
- Covers classical ML properly, not just neural nets
Awesome Machine Learning
The long-standing curated index of machine learning frameworks, libraries and software, organised by programming language — the reference people have used for a decade.
- Maintained for over a decade
- Organised by language, not by hype
- The reference the whole field points to
Key differences
Hands-On Machine Learning is book notebooks, while Awesome Machine Learning is curated list. Their licenses differ (Apache-2.0 vs CC0-1.0), which matters if you ship a commercial product. Hands-On Machine Learning leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, Hands-On Machine Learning fits the classic path from scikit-learn to deep learning, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Awesome Machine Learning for finding the right library in any language.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is Hands-On Machine Learning or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while Hands-On Machine Learning rewards more setup with more control.
Are Hands-On Machine Learning and Awesome Machine Learning free?
Hands-On Machine Learning is free and open source (Apache-2.0), and Awesome Machine Learning is free and open source (CC0-1.0). Neither charges for the core software.
Can I run Hands-On Machine Learning and Awesome Machine Learning locally?
Hands-On Machine Learning: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Hands-On Machine Learning vs Awesome Machine Learning — which should I pick in 2026?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Awesome Machine Learning for finding the right library in any language.
People also compare
Explore more open-source AI
Browse thousands of open-source AI tools, models and projects — all curated in one place, updated daily.
Explore the directory →