Learning Paths
Four progressive paths, every step tied to a hand-checked resource.
Zero to AI: Beginner Path
From Python basics to understanding LLMs — build the foundation first, skip the hype
Finish able to handle basic data processing in Python, understand core machine learning and deep learning concepts, follow mainstream LLM discussions, and complete a small practice project on your own.
View path →AI App Development Path
From your first API call to a deployed RAG application
Finish able to call mainstream LLM APIs, write solid prompts, use frameworks like LangChain/LlamaIndex or low-code platforms like Dify, build a knowledge-base RAG app, ship a shareable demo UI and deploy it.
View path →AI Agent Development Path
From tool calling to multi-agent collaboration — build AI that gets work done
Finish able to design an agent's toolset and workflow, orchestrate complex flows with LangGraph, understand the MCP protocol, and build agent apps with evaluation and security awareness.
View path →AI Engineer Path
From data prep to LLMOps — complete production-grade engineering skills
Finish able to own the full loop from data prep, model training/calling and evaluation to deployment and monitoring, reliably pushing ML or LLM apps to production.
View path →