AI Engineer Certification
A complete, zero-to-hireable path: 19 courses, 151 chapters, each with a runnable lab and a check for understanding. Everything runs offline. The companion code and labs are open on GitHub. Free, no login, no email.
- Course 08 ch
Foundations: Python and Math for AI
From zero. Real Python you run yourself and only the math you need (vectors, matrices, probability, softmax, gradients). 8 chapters, live.
- Course 110 ch
LLM Fundamentals: Build a Tiny LLM From Scratch
Build a real language model by hand: tokenizer, embeddings, attention, transformer, training loop, generation. The depth vertical. 10 chapters, live.
- Course 28 ch
Prompt Engineering
The first lever and highest-frequency daily skill: few-shot, chain-of-thought, structured output, caching, and eval-driven optimization.
- Course 38 ch
RAG and Embeddings
The #1 production pattern (70% of teams). Embeddings, chunking, vector search, re-ranking, end-to-end RAG, and the failure modes that bite.
- Course 48 ch
Agents, Tools and MCP
The 2026 frontier: the ReAct loop, tool and function calling, memory, and the Model Context Protocol (build an MCP server).
- Course 58 ch
Evaluation and Testing
Evals replace unit tests. Golden datasets, code graders vs LLM-as-judge, regression gates, hallucination scoring. Table-stakes.
- Course 68 ch
LLM Application Engineering
The literal daily job: APIs and SDKs, streaming, structured output, retries and rate limits, cost control, multi-provider routing, observability.
- Course 78 ch
Training and Fine-tuning
Shape a base model: datasets, loss curves, SFT and LoRA/QLoRA, DPO, and the judgment of when to fine-tune vs prompt vs RAG.
- Course 88 ch
Transformers Deep Dive
Explain the internals cold: positional encodings (RoPE), attention variants, BPE, KV cache, scaling laws. The T-shape depth vertical.
- Course 98 ch
AI Engineering in Production
Ship and operate: serving, quantization (AWQ/GGUF), vLLM/KV cache, eval-gated CI/CD, cost and drift monitoring, LLMOps.
- Course 108 ch
AI Safety and Security
The OWASP LLM Top 10, prompt-injection offense and defense, guardrails, and red-teaming your own app. Ada's differentiator.
- Course 116 ch
Multimodal AI
Vision-language, image generation, speech (STT/TTS), and multimodal RAG. The converging edge.
- Course 128 ch
Interview Prep and System Design
Ace the interview: the real loop, from-scratch coding, LLM system design, take-home patterns, behavioral, and question banks.
- Course 137 ch
Capstone Projects
What gets you hired: 3-5 deployed, evaluated builds. RAG assistant, tool-calling agent, eval pipeline, MCP assistant, self-red-teamed app.
- Course 148 ch
Claude Code and Agentic Builds
Build agents the way Claude Code does: the agentic loop, the tool contract, ReAct, subagents and delegation, MCP, hooks and guardrails, orchestrating a fleet, and a complete agentic workflow. 8 chapters, offline labs.
- Course 158 ch
Claude Code SDK and API
Drive Claude from code: the Messages API and Agent SDK end to end. Typed responses, system and roles, streaming, tool use, the agent loop (tool_runner), structured output, prompt caching and token counting, then a small shippable app.
- Course 168 ch
CCA-F Certification Prep
Pass the Claude certification: the weighted exam domains, scenarios, and recurring wrong-answer traps, each drilled with a runnable lab and a mock exam scored at the real pass line. 8 chapters.
- Course 178 ch
LifeOS: Setup to Advanced
The personal AI operating system end to end: current state to ideal state, install, TELOS, the Algorithm, skills, hooks, Pulse, and composing it into a daily OS. 8 chapters.
- Course 188 ch
LifeOS Mastery: The Complete Setup
The flagship LifeOS course, taught the way its creator teaches it: why before how, and the full personalized setup end to end. Install and upgrade, name your DA, identity and a quantified 12-trait personality, TELOS in two parts, the /interview, migrate your context, and run it all through the Algorithm as a daily OS. 8 chapters, offline labs.