Learning Paths
Choose the starting point that matches where you are today. Each path is broken into phases, checkpoints, and lessons that make the next step clear.
Software Engineer to ML/AI Engineer
A 34-module transition path for software engineers learning ML systems, LLM apps, agent engineering (context, harnesses, MCP, evals, security), MLOps, and AI system design.
5 phases · 34 modules · ~4 hrs
Experienced software engineers who can already ship production software and need a direct bridge into probabilistic, data-dependent ML/AI work.
View roadmap →Curious to AI-Fluent
A 25-module path for learning how AI works, how to use AI tools and agents well, and how to build a first AI project without a programming background.
5 phases · 25 modules · ~2 hrs
People with no programming or data science background: product managers, domain experts, business analysts, marketers, researchers, and anyone who wants to understand and use AI tools with judgment.
View roadmap →College Student to ML/AI Engineer
A 35-module roadmap from programming basics to ML projects, AI product systems, agent engineering, production workflows, and interview practice.
5 phases · 35 modules · ~4 hrs
College students and early-career learners who know basic programming and need a clear, cumulative route into ML/AI engineering.
View roadmap →