Learning Paths
Structured curricula to become an AI engineering expert
Career Track
LLM Engineer Path
Master production-ready LLM application development. From transformer fundamentals to deploying RAG systems, fine-tuning, and AI agents. Based on industry best practices from OpenAI, Anthropic, and leading AI labs.
MLOps Engineer Path
Learn to build, deploy, and operate machine learning systems at scale. Master the complete ML lifecycle from experiment tracking to production monitoring. Based on practices from top ML teams at Google, Meta, and Netflix.
ML Researcher Path
Build deep theoretical foundations for machine learning research. Master the mathematics, methodology, and skills needed for publishing papers and advancing the field. Designed for those pursuing research roles or PhDs.
Data Scientist Path
Master the complete data science toolkit from statistics to machine learning to communication. Learn to extract insights from data and drive business decisions. Covers the essential 95% of what data scientists do daily.
Full-Stack AI Engineer Path
Become a versatile AI engineer who can build end-to-end AI-powered applications. From frontend to backend to ML models - own the entire stack. Perfect for startups and product teams.
AI Agent Developer Path
Specialize in building autonomous AI agents that can reason, plan, and execute complex tasks. Master frameworks like LangChain, LangGraph, CrewAI, and AutoGen to build sophisticated multi-agent systems.
Prompt Engineer Path
Master the art and science of prompt engineering. Learn to design effective prompts for any task, build prompt libraries, and optimize LLM outputs systematically. A specialized skill increasingly in demand.