Hugging Face Course vs 
Applied ML
Hugging Face Course vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. Master transformers with the actual library vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
Hugging Face Course vs Applied ML at a glance
| Spec | Hugging Face Course | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Course | Curated papers & posts |
| License | Apache-2.0 | MIT |
| Runs locally | Yes | Yes |
| Primary language | Python | Markdown |
| Ease of use | Intermediate | Intermediate |
| Best for | learning the library the whole ecosystem uses | learning from what companies really did |
| GitHub stars | 4.1k | 29.9k |
How Hugging Face Course and Applied ML score
| Criterion | Hugging Face Course | Applied ML |
|---|---|---|
| Popularity | 2.5 | 3.5 |
| Maintenance | 5.0 | 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
Hugging Face Course
The official Hugging Face course on transformers, datasets and tokenizers — you learn the ecosystem that most of open-source AI actually runs on.
- Teaches the library everyone actually uses
- Free, with Colab notebooks throughout
- Maintained by the people who wrote the library
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
Hugging Face Course is course, 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, Hugging Face Course fits learning the library the whole ecosystem uses, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose Hugging Face Course for learning the library the whole ecosystem uses. 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 Hugging Face 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 Hugging Face Course and Applied ML free?
Hugging Face 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 Hugging Face Course and Applied ML locally?
Hugging Face Course: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Hugging Face Course vs Applied ML — which should I pick in 2026?
Choose Hugging Face Course for learning the library the whole ecosystem uses. Choose Applied ML for learning from what companies really did.
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