Guided learning paths

Courses.

A course is a guided learning path: connected lessons that build your understanding of a subject, step by step.

16 courses · pick one and read in order
  1. 11 modules · 156 lessonsLLM ConceptsFrom transformer architecture to cutting-edge research — each concept explained with intuition, math, and connections to the bigger picture.
  2. 10 modules · 90 lessonsAI Agent ConceptsFoundations of autonomous AI agents — reasoning, planning, memory, tool use, multi-agent systems, and safety.
  3. 10 modules · 72 lessonsAI Agent EvaluationBenchmarks, automated evaluation methods, trajectory analysis, and production monitoring for AI agents.
  4. 1 modules · 12 lessonsAgentic Design PatternsArchitecture selection, tool design, error resilience, multi-agent coordination, and production patterns for agentic systems.
  5. 13 modules · 120 lessonsComputer Vision ConceptsImage fundamentals through CNNs, object detection, segmentation, generative models, vision transformers, and 3D vision.
  6. 10 modules · 49 lessonsLangGraph AgentsBuild production AI agents with LangGraph — tools, memory, human-in-the-loop, streaming, multi-agent systems, and deployment.
  7. 15 modules · 105 lessonsLLM EvolutionThe history and trajectory of large language models — from pre-transformer foundations through the 2025 frontier.
  8. 12 modules · 82 lessonsMachine Learning FoundationsMathematical foundations, learning theory, supervised and unsupervised methods, neural networks, and production ML systems.
  9. 1 modules · 14 lessonsBuilding MCP Servers with SupabaseA hands-on guide to building Model Context Protocol servers with Supabase — from architecture to production deployment.
  10. 12 modules · 95 lessonsNatural Language ProcessingText preprocessing, representation, sequence models, NLP tasks, information extraction, and multilingual NLP.
  11. 9 modules · 79 lessonsPrompt EngineeringCore prompting techniques, reasoning elicitation, system prompts, structured output, context engineering, and production safety.
  12. 8 modules · 57 lessonsReinforcement LearningFoundations through deep RL, policy gradients, model-based methods, RL for language models, and landmark applications.
  13. 10 modules · 42 lessonsBuilding a Multi-Skill AI AgentHands-on guide to building an AI agent with multiple skills — architecture, tool design, orchestration, error handling, and a capstone research agent project.
  14. 8 modules · 70 lessonsAgent Harnesses & OrchestrationThe harness layer above LLMs — Claude Agent SDK, Codex CLI, Cursor, ruflo, LangGraph, AutoGen, CrewAI, and OpenAI Agents SDK compared concept-by-concept. Topologies, consensus, federation, planning, and the orchestration plumbing that turns models into systems.
  15. 5 modules · 8 lessonsAdvanced LLM ConceptsA second-volume tour of the techniques pushing large language models forward — advanced training, modern inference and serving, retrieval and embeddings, alignment, and adversarial robustness.
  16. 8 modules · 33 lessonsData Engineering for AI Agents on GCPThe pattern that makes agents trustworthy: ingest external data into a Cloud Storage lake, refine it through BigQuery, and serve it to agents via structured and semantic retrieval. End-to-end on Google Cloud, from raw bytes to agent context — with a curated 2024–2026 research reading list.