ML for Beginners vs 
LLMs from Scratch
ML for Beginners vs LLMs from Scratch compared for 2026 — features, license, ease of use, performance and which one to choose. Microsoft's classic machine learning course vs Build a GPT from nothing, line by line.
Updated regularly · curated by OpenSourceAI.tech
ML for Beginners vs LLMs from Scratch at a glance
| Spec | ML for Beginners | LLMs from Scratch |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Curriculum (12 weeks) | Book + code |
| License | MIT | Apache-2.0 |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Python |
| Ease of use | Beginner | Intermediate |
| Best for | anyone starting ML without a maths background | genuinely understanding how an LLM works |
| GitHub stars | 88k | 99k |
How ML for Beginners and LLMs from Scratch score
| Criterion | ML for Beginners | LLMs from Scratch |
|---|---|---|
| Popularity | 4.5 | 4.5 |
| Maintenance | 5.0 | 5.0 |
| 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
LLMs from Scratch
Sebastian Raschka's companion repository to "Build a Large Language Model (From Scratch)": you implement attention, a transformer, pretraining and fine-tuning yourself, in plain PyTorch.
- You build every component yourself — no black boxes
- Runs on a laptop, no cluster needed
- The clearest explanation of attention anywhere
Key differences
ML for Beginners is curriculum (12 weeks), while LLMs from Scratch is book + code. Their licenses differ (MIT vs Apache-2.0), which matters if you ship a commercial product. ML for Beginners leans more beginner-friendly, whereas LLMs from Scratch is more suited to intermediate users. In short, ML for Beginners fits anyone starting ML without a maths background, and LLMs from Scratch fits genuinely understanding how an LLM works.
Which should you choose?
Choose ML for Beginners for anyone starting ML without a maths background. Choose LLMs from Scratch for genuinely understanding how an LLM works.
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 LLMs from Scratch easier to use?
ML for Beginners is generally the easier of the two to get started with, while LLMs from Scratch rewards more setup with more control.
Are ML for Beginners and LLMs from Scratch free?
ML for Beginners is free and open source (MIT), and LLMs from Scratch is free and open source (Apache-2.0). Neither charges for the core software.
Can I run ML for Beginners and LLMs from Scratch locally?
ML for Beginners: yes · LLMs from Scratch: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
ML for Beginners vs LLMs from Scratch — which should I pick in 2026?
Choose ML for Beginners for anyone starting ML without a maths background. Choose LLMs from Scratch for genuinely understanding how an LLM works.
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