Courses
Data Engineering for AI Agents on GCP
— The 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. (8 modules, 33 concepts)
Advanced LLM Concepts
— A second-volume tour of the techniques pushing large language models forward — advanced training, modern inference and serving, retrieval and embeddings, alignment, and adversarial robustness. (5 modules, 8 concepts)
Agent Harnesses & Orchestration
— The 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. (8 modules, 70 concepts)
Building a Multi-Skill AI Agent
— Hands-on guide to building an AI agent with multiple skills — architecture, tool design, orchestration, error handling, and a capstone research agent project. (10 modules, 42 concepts)
Reinforcement Learning
— Foundations through deep RL, policy gradients, model-based methods, RL for language models, and landmark applications. (8 modules, 57 concepts)
Prompt Engineering
— Core prompting techniques, reasoning elicitation, system prompts, structured output, context engineering, and production safety. (9 modules, 79 concepts)
Natural Language Processing
— Text preprocessing, representation, sequence models, NLP tasks, information extraction, and multilingual NLP. (12 modules, 95 concepts)
Building MCP Servers with Supabase
— A hands-on guide to building Model Context Protocol servers with Supabase — from architecture to production deployment. (1 modules, 14 concepts)
Machine Learning Foundations
— Mathematical foundations, learning theory, supervised and unsupervised methods, neural networks, and production ML systems. (12 modules, 82 concepts)
LLM Evolution
— The history and trajectory of large language models — from pre-transformer foundations through the 2025 frontier. (15 modules, 105 concepts)
LangGraph Agents
— Build production AI agents with LangGraph — tools, memory, human-in-the-loop, streaming, multi-agent systems, and deployment. (10 modules, 49 concepts)
Computer Vision Concepts
— Image fundamentals through CNNs, object detection, segmentation, generative models, vision transformers, and 3D vision. (13 modules, 120 concepts)
Agentic Design Patterns
— Architecture selection, tool design, error resilience, multi-agent coordination, and production patterns for agentic systems. (1 modules, 12 concepts)
AI Agent Evaluation
— Benchmarks, automated evaluation methods, trajectory analysis, and production monitoring for AI agents. (10 modules, 72 concepts)
AI Agent Concepts
— Foundations of autonomous AI agents — reasoning, planning, memory, tool use, multi-agent systems, and safety. (10 modules, 90 concepts)
LLM Concepts
— From transformer architecture to cutting-edge research — each concept explained with intuition, math, and connections to the bigger picture. (11 modules, 156 concepts)