LLM Course vs 
Awesome Machine Learning
LLM Course vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. The reference roadmap for learning LLMs vs The reference index of ML libraries, by language.
Updated regularly · curated by olud.ai
LLM Course vs Awesome Machine Learning at a glance
| Spec | LLM Course | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Course + roadmap | Curated list |
| License | Apache-2.0 | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | going from using LLMs to actually training them | finding the right library in any language |
| GitHub stars | 81.1k | 73.6k |
How LLM Course and Awesome Machine Learning score
| Criterion | LLM Course | Awesome Machine Learning |
|---|---|---|
| Popularity | 4.5 | 4.5 |
| Maintenance | 4.0 | 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
LLM Course
Maxime Labonne's course splits LLM learning into three tracks — the fundamentals, building an LLM, and deploying one — with Colab notebooks for fine-tuning, quantisation and RLHF.
- The clearest LLM roadmap that exists
- Colab notebooks you can run without a GPU
- Covers fine-tuning, quantisation and RLHF hands-on
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
LLM Course is course + roadmap, 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. LLM Course leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, LLM Course fits going from using LLMs to actually training them, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose LLM Course for going from using LLMs to actually training them. 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 LLM Course or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while LLM Course rewards more setup with more control.
Are LLM Course and Awesome Machine Learning free?
LLM Course 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 LLM Course and Awesome Machine Learning locally?
LLM Course: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LLM Course vs Awesome Machine Learning — which should I pick in 2026?
Choose LLM Course for going from using LLMs to actually training them. Choose Awesome Machine Learning for finding the right library in any language.
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