Structured LLM Learning Path, from Zero to AI Researcher, 8-Phase Curriculum
A new structured curriculum has been introduced for mastering Large Language Models (LLMs) and LLM-based agents. The program spans approximately five months and is designed for self-paced learning, with the possibility of completion in three months for those with prior deep learning experience. It consists of eight phases covering foundational topics, practical exercises, and advanced research.
- ▪The curriculum is divided into eight phases, each focusing on different aspects of LLMs.
- ▪Participants are encouraged to track their progress and complete hands-on exercises for effective learning.
- ▪Prerequisites include intermediate Python programming and basic knowledge of linear algebra, calculus, and probability.
Opening excerpt (first ~120 words) tap to expand
🎓 LLM Learning Path — From Zero to Researcher A structured, self-paced curriculum for mastering Large Language Models and LLM-based Agents. Timeline: ~5 months (can be shortened to ~3 months with prior DL experience) 📋 Curriculum Overview Phase Topic Duration Status 1 Foundations 2-3 weeks ⬜ 2 Transformers 2-3 weeks ⬜ 3 Pre-training & Scaling 2-3 weeks ⬜ 4 Fine-Tuning & Alignment 2-3 weeks ⬜ 5 Inference & Deployment 1-2 weeks ⬜ 6 Prompting & Reasoning 1-2 weeks ⬜ 7 LLM Agents 2-4 weeks ⬜ 8 Advanced Research Ongoing ⬜ 🗺️ How to Use This Repo Go phase by phase — each folder has its own README with objectives, readings, and exercises Check off items as you complete them (edit the checkboxes in each phase) Take notes in the notes/ folder — one file per phase Do the exercises — hands-on…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.