Hands-On Machine Learning vs 
Applied ML
Hands-On Machine Learning vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. The notebooks of the best-selling ML book vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
Hands-On Machine Learning vs Applied ML at a glance
| Spec | Hands-On Machine Learning | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Book notebooks | Curated papers & posts |
| License | Apache-2.0 | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Intermediate |
| Best for | the classic path from scikit-learn to deep learning | learning from what companies really did |
| GitHub stars | — | 29.9k |
How Hands-On Machine Learning and Applied ML score
| Criterion | Hands-On Machine Learning | Applied ML |
|---|---|---|
| Popularity | n/a | 3.5 |
| Maintenance | n/a | 2.0 |
| Ease of use | 3.5 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 5.0 |
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
Applied ML
Eugene Yan's curated collection of papers and engineering blog posts on how companies actually build and deploy ML systems in production — organised by problem, not by algorithm.
- Real production systems, not toy examples
- Organised by problem, not by algorithm
- Curated by a practising ML engineer
Key differences
Hands-On Machine Learning is book notebooks, while Applied ML is curated papers & posts. Their licenses differ (Apache-2.0 vs MIT), which matters if you ship a commercial product. In short, Hands-On Machine Learning fits the classic path from scikit-learn to deep learning, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Applied ML for learning from what companies really did.
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 Applied ML easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are Hands-On Machine Learning and Applied ML free?
Hands-On Machine Learning is free and open source (Apache-2.0), and Applied ML is free and open source (MIT). Neither charges for the core software.
Can I run Hands-On Machine Learning and Applied ML locally?
Hands-On Machine Learning: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Hands-On Machine Learning vs Applied ML — which should I pick in 2026?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Applied ML for learning from what companies really did.
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