LLM Course vs 
Applied ML
LLM Course vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. The reference roadmap for learning LLMs vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
LLM Course vs Applied ML at a glance
| Spec | LLM Course | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Course + roadmap | Curated papers & posts |
| License | Apache-2.0 | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Intermediate | Intermediate |
| Best for | going from using LLMs to actually training them | learning from what companies really did |
| GitHub stars | 80.9k | 29.9k |
How LLM Course and Applied ML score
| Criterion | LLM Course | Applied ML |
|---|---|---|
| Popularity | 4.5 | 3.5 |
| Maintenance | 4.0 | 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
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
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
LLM Course is course + roadmap, 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, LLM Course fits going from using LLMs to actually training them, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose LLM Course for going from using LLMs to actually training them. 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 LLM Course or Applied ML easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are LLM Course and Applied ML free?
LLM Course 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 LLM Course and Applied ML locally?
LLM Course: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LLM Course vs Applied ML — which should I pick in 2026?
Choose LLM Course for going from using LLMs to actually training them. Choose Applied ML for learning from what companies really did.
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