Zero to AI
AI Learning Path
Work through Python, data science, LLMs, agents, evaluation, and production systems in one connected learning path with hands-on notebooks and fast-reference guides.
Core path
35+
Top-level modules spanning fundamentals to production AI systems.
Practice
Notebooks + quizzes
Learn with executable examples, challenge pages, and end-of-section checks.
Reference
Cheatsheets
Keep the docs open while building with quick command and architecture references.
New here
Start from zero
Set up your environment, learn the repo structure, and enter the core sequence in the intended order.
Go to course setup →Hot path
Build AI agents
Jump into function calling, ReAct, MCP, reasoning models, and agent evaluation if you already know the basics.
Explore the agents track →Fast reference
Use the cheatsheets
Keep Python, cloud, Kubernetes, GitHub Actions, Docker, and MLOps references close while you build.
Open the reference hub →Recommended sequence
Follow the curriculum by phase
Phase 1
Foundations
Build the baseline: setup, Python, data science, and math needed for everything else.
Phase 2
LLM and agent systems
Move from tokens and embeddings into RAG, fine-tuning, multimodal systems, local models, and agents.
Phase 3
Production and specialization
Add evaluation, safety, real-time systems, advanced deep learning, reinforcement learning, and practical specializations.
Build mode
Use the docs the way you actually learn
Quizzes
Pressure-test understanding before you move deeper into the curriculum.
Cheatsheets
Keep command references and architecture notes open while you work.
References
Use curated references when you need source material, not just tutorials.
Glossary
Bridge the gap between reading fast and understanding the vocabulary.
Quick links
- AI-powered dev tools
- Real-time streaming systems
- Inference optimization
- AI hardware and LLM validation
- Interactive app demos
This site works best as a progression: use the roadmap for direction, notebooks for practice, and cheatsheets for speed.