ML for Beginners vs 
Applied ML
ML for Beginners vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. Microsoft's classic machine learning course vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
ML for Beginners vs Applied ML at a glance
| Spec | ML for Beginners | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Curriculum (12 weeks) | Curated papers & posts |
| License | MIT | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Beginner | Intermediate |
| Best for | anyone starting ML without a maths background | learning from what companies really did |
| GitHub stars | 88k | 29.9k |
How ML for Beginners and Applied ML score
| Criterion | ML for Beginners | Applied ML |
|---|---|---|
| Popularity | 4.5 | 3.5 |
| Maintenance | 5.0 | 2.0 |
| Ease of use | 5.0 | 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
ML for Beginners
A 12-week, 26-lesson curriculum from Microsoft covering classical machine learning with scikit-learn, built around hands-on projects rather than theory dumps.
- Project-based: you build things from lesson one
- Quizzes and assignments, not just reading
- Available in dozens of languages
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
ML for Beginners is curriculum (12 weeks), while Applied ML is curated papers & posts. ML for Beginners leans more beginner-friendly, whereas Applied ML is more suited to intermediate users. In short, ML for Beginners fits anyone starting ML without a maths background, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose ML for Beginners for anyone starting ML without a maths background. 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 ML for Beginners or Applied ML easier to use?
ML for Beginners is generally the easier of the two to get started with, while Applied ML rewards more setup with more control.
Are ML for Beginners and Applied ML free?
ML for Beginners is free and open source (MIT), and Applied ML is free and open source (MIT). Neither charges for the core software.
Can I run ML for Beginners and Applied ML locally?
ML for Beginners: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
ML for Beginners vs Applied ML — which should I pick in 2026?
Choose ML for Beginners for anyone starting ML without a maths background. Choose Applied ML for learning from what companies really did.
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