Learning Path
The curriculum is seven sections, and each one leans on the ones before it. If you're not sure what to read next, go top to bottom. If you already know a section (say you're comfortable with the math), skip it; the order is a suggestion, not a gate.
The counts under each section come from the "Mark as understood" button at the bottom of every page. That's stored in your browser only, and the full checklist is on Progress.
Foundations
Python, systems basics, and the math the rest of the site leans on.
Getting Started · CS Fundamentals for AI Engineers · Python Engineering for AI · Mathematics for AI
19 pages
Models
Classical ML, deep learning, vision, NLP, speech, LLMs, graph ML, and RL.
Machine Learning · Deep Learning · Computer Vision · NLP · Speech & Audio AI · LLMs & GenAI · Graph ML · Reinforcement Learning
86 pages
Agents & Applications
Agents, and AI applied to science, healthcare, and other domains.
Agents · AI for Science · Domain AI Applications
26 pages
Systems & Infrastructure
Designing, shipping, and running ML systems: MLOps, databases, frameworks.
ML System Design · MLOps · Databases · Frameworks · CLI Reference
59 pages
Safety & Evaluation
Evaluation, security, alignment, and interpretability.
AI Evaluation · AI Security · AI Safety & Alignment · Interpretability
18 pages
Research & Build
Reading papers, building models from scratch, and full projects.
Research Engineering · Build From Scratch · Projects · Visual Lab · Practice Problems
28 pages
Career
Interview prep, roadmaps, and resources.
Interview Prep · Roadmaps · Resources
17 pages
If you'd rather work backwards from a job, Suggested Paths orders topics by how often they come up in ML and AI job postings.