Hands-On Machine Learning vs 
Awesome LLM
Hands-On Machine Learning vs Awesome LLM compared for 2026 — features, license, ease of use, performance and which one to choose. The notebooks of the best-selling ML book vs Papers, models and tools of the LLM era.
Updated regularly · curated by OpenSourceAI.tech
Hands-On Machine Learning vs Awesome LLM at a glance
| Spec | Hands-On Machine Learning | Awesome LLM |
|---|---|---|
| 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 | getting your bearings in the LLM landscape |
| GitHub stars | — | 27.1k |
How Hands-On Machine Learning and Awesome LLM score
| Criterion | Hands-On Machine Learning | Awesome LLM |
|---|---|---|
| Popularity | n/a | 3.5 |
| Maintenance | n/a | 3.0 |
| 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 LLM
A curated index of the LLM landscape: the foundational papers, the open models, the training and serving tools — updated as the field moves.
- Tracks papers, models and tools in one place
- Updated as the field moves
- Good entry point into the research
Key differences
Hands-On Machine Learning is book notebooks, while Awesome LLM 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 LLM is more suited to beginner users. In short, Hands-On Machine Learning fits the classic path from scikit-learn to deep learning, and Awesome LLM fits getting your bearings in the LLM landscape.
Which should you choose?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Awesome LLM for getting your bearings in the LLM landscape.
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 LLM easier to use?
Awesome LLM 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 LLM free?
Hands-On Machine Learning is free and open source (Apache-2.0), and Awesome LLM is free and open source (CC0-1.0). Neither charges for the core software.
Can I run Hands-On Machine Learning and Awesome LLM locally?
Hands-On Machine Learning: yes · Awesome LLM: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Hands-On Machine Learning vs Awesome LLM — which should I pick in 2026?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Awesome LLM for getting your bearings in the LLM landscape.
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