#8181st of 100 in Learning and cookbooks
ai-engineer-headquarters
Self-paced AI engineering curriculum from ML basics to RAG, fine-tuning and agents
- Stars
- 3.8k
- License
- no license
- Last commit
- Oct 2026
- Language
- Jupyter Notebook
3,640 on 22 Apr 20263,779 on 11 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A study curriculum laid out as a path: prep, AI engineering toolkit, ML and MLOps, LLMs, production RAG, fine-tuning, autonomous agents, career, and bonus masterclasses. Content comes as video sessions or text notes, with a suggested daily routine of 4 hours deep work and 2 hours shallow work. The repository is mostly Jupyter Notebooks; the README does not list specific libraries or exercises.
Who it is for: Students and professionals following a structured AI engineering study plan
Strengths
- Covers a full path from ML basics through RAG, fine-tuning and agents
- Offers both video sessions and text notes for the same topics
- Notebook-based content can be run and edited directly
Weaknesses
- No license file, so reuse and redistribution rights are unclear
- README gives no per-module details, tools or prerequisites
- Much of the README is motivational routine, not technical content
- Path list numbers two items as 8, so the structure is unclear
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| 1 | ai-engineering-from-scratchHands-on AI engineering curriculum with 542 lessons across 20 phases | 91 out of 100 |
| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 83 out of 100 |
| 3 | rag_techniquesCollection of 42 runnable notebooks implementing RAG techniques | 82 out of 100 |
| #4 | cs249r_bookOpen textbook and course materials for machine learning systems engineering | 82 out of 100 |
| #5 | ai-for-beginners24-lesson AI curriculum with notebooks in PyTorch and TensorFlow | 81 out of 100 |