All AI Courses
12 self-paced, practical tracks from Linux & terminal foundations to multi-agent graph orchestration and production RAG.
Prerequisite
Practical Fundamentals
Linux and the terminal, Python environments, git, Postgres and Docker, and setting up an AI coding toolkit. No prior ML experience required.
Start Here
Vibe Coding - From Natural Language to Production
Build real applications by describing what you want in plain English. No coding required - just clear thinking and the right patterns.
Foundations
Prompt Engineering - From First Principles to Production
Design, test, and optimize prompts as an engineering discipline - zero-shot, chain-of-thought, structured output, RAG, safety guardrails, and prompt libraries at scale.
Protocols
Model Context Protocol (MCP) in Practice
Connect AI models to local databases, enterprise APIs, filesystems, and developer tools using the open MCP standard with security sandboxing.
Coding Agent
Claude Code - From First Commit to Agent Teams
Anthropic's autonomous coding agent - print mode, interactive sessions, skills, hooks, subagents, MCP, CI/CD, dynamic workflows, and the Agent SDK.
Google Ecosystem
Google AI Coding Tools: Gemini CLI, Antigravity & Beyond
Google's agentic coding ecosystem - Gemini CLI, Antigravity IDE, Antigravity CLI, SDK, parallel agents, skills, browser-in-the-loop, and enterprise deployment.