Learn to design, build, and ship real AI systems — from ML/deep learning fundamentals through transformers, LLMs, prompt engineering, AI agents and multi-agent systems, RAG, fine-tuning, deployment, and AI safety. 21 modules from fundamentals through certification, 5 complete real-project builds (a customer support RAG chatbot, a multi-agent research & report assistant, a fine-tuned support-ticket classifier, a production LLM inference service, an AI-powered engineering defect detection pipeline), a 12-template documentation kit, and a certificate of completion. One-time purchase, no account required.
Explore the Full Curriculum →No. There is no government license for AI engineering. Competence is demonstrated through vendor and platform certifications (Microsoft Azure, NVIDIA) and a portfolio of deployed AI applications and agents.
If you build generative-AI applications on Azure, the AI Engineer Associate (AI-102) is the standard. If you need deep-learning and accelerated-computing fundamentals (CUDA, GPUs), NVIDIA's DLI certifications are the specialist option. For modeling-focused data-science certifications (AWS ML Specialty, GCP ML Engineer, TensorFlow, Databricks), see the AI and Data Science Studio instead.
Most cloud AI certifications are closed-book proctored exams. NVIDIA DLI certifications are course-based, hands-on lab assessments instead.
Requirements vary by vendor and track, but there are no hard prerequisites for most of these — you can sit the exams whenever you are ready.
How EEs use AI for NEC load calculations, protection coordination, PV sizing, arc flash analysis, and construction phase automation.
Deep-dive into transformer architecture, self-attention math, scaling laws, LoRA fine-tuning, RLHF, RAG pipelines, and production inference.
GPUs, TPUs, NPUs, ASICs, CUDA, HBM memory bandwidth, NVLink, cloud pricing, and the full AI software stack from silicon to PyTorch.
From AI-900 to AI Architect — every major cert track with exam costs, study hours, free resources, and a 90-day study plan.
Master prompting for ChatGPT, Claude, and Gemini — zero-shot, few-shot, chain-of-thought, role personas, templates, and AI freelancing.
Build AI agents and automation pipelines with n8n, Make.com, and LangChain. Agent architecture, vector memory, error handling, and monetization.
Complete engineering case study: problem scoping, data pipeline, transformer architecture, pretraining, SFT, instruction tuning, RLHF/DPO, LoRA, RAG, safety evaluation, and production deployment with vLLM.
Engineering-grade agent design: prompts as programs, memory and RAG, tool/function calling, ReAct and Planner-Executor planning, self-correction, and a full production Python agent skeleton.
Platform-agnostic guide to AI agent systems: design patterns, LangChain/LangGraph, CrewAI, LlamaIndex, Google Opal, Copilot Studio, Vertex AI Agent Builder, platform selection matrix, and security/governance.
Real production AI agent build: FastAPI + PyMuPDF + Pydantic + PostgreSQL + pgvector RAG generating federal RFP compliance matrices. Chunking, clause detection, async Celery processing, and deployment.
Real production AI agent build: Next.js 15 + FastAPI + LangGraph + Celery + PostgreSQL drafting full consulting proposals. 13-node pipeline, async processing, and a human approval gate before export.
Deep n8n-specific guide: nodes, triggers, and JSON data flow, real CRM/invoicing/support-routing workflow builds, the AI Agent node with LLMs and RAG, error handling, rate limiting, and queue-mode scaling.
Beginner's guide to Claude Cowork, Anthropic's AI desktop automation agent — installation, your first task, file organization, document/spreadsheet generation, browser automation, connectors, plugins, and a scheduled Twitter bot case study.
Why a model that's near-perfect on training data can be a red flag, not a reward. Illustrated comparison of underfitting, a good fit, and overfitting, plus the training-vs-validation-error curve that reveals the ideal stopping point.
Three different answers to "how do I make this LLM know what I need?" Illustrated side-by-side pipelines plus a decision framework for picking the right tool — and why fine-tuning a model just to "teach it facts" is one of the most common, most expensive mistakes.
Three completely different numbers people conflate when talking about LLM "size." Illustrated breakdown of what's fixed at training, what's a fixed architectural limit, and what actually varies with every single request — plus why a huge model can ship with a small context window, or the reverse.
Two dials that both get called "randomness" but do fundamentally different jobs. Illustrated comparison of temperature reshaping the entire probability distribution vs. top-p cutting the candidate pool outright — and why serious sampling setups tune both together.
Why showing the model examples changes what it's actually doing — not just how much it knows. Illustrated comparison of instructions-alone producing inconsistent output formats vs. worked examples pinning down the exact format through pattern-matching, not interpretation.
Why a deployed model doesn't learn from your conversations (usually). Illustrated comparison of training — enormously expensive, weights actually adjusted via computed gradients — vs. inference — cheap, fast, weights read but never modified — plus why in-context conversation memory is a separate, temporary mechanism.
Both get called "numbers representing text," but only one of them knows what the text means. Illustrated comparison of token IDs — arbitrary vocabulary indices with no semantic structure — vs. embeddings — learned vectors where numerical closeness reflects actual meaning — and why semantic search and RAG compare embeddings, never raw token IDs.
Why "we added RAG" doesn't mean hallucination is solved. Illustrated comparison of an ungrounded answer generated with no source material vs. a grounded (RAG) answer — plus the retrieval-miss and misinterpretation cases where a grounded system still fabricates, now with false confidence borrowed from seemingly credible context.
Why asking a model to "show its work" measurably improves accuracy on multi-step tasks, at the direct cost of more latency and tokens. Illustrated comparison of a direct query-to-answer arrow vs. a query-through-steps-to-answer chain — plus why a fluent, plausible-looking reasoning trace still isn't proof the model's steps are logically valid.
Both get called "shrinking the model," but only one actually changes what the model is. Illustrated teacher-to-student training pipeline vs. a same-architecture weight matrix re-encoded to lower precision — plus why the two stack in production instead of substituting for each other.
Why calling every multi-step AI system an "agent" blurs the distinction that actually matters. Illustrated comparison of a workflow's fixed directed graph, drawn by a human in advance, vs. an agent's dynamic plan-act-observe-replan loop, where the model itself decides the next step at runtime.
MCP didn't invent a new way for models to use tools — it standardized how tools get exposed to them. Illustrated comparison of a model's native tool-call schema wired directly into one app's code vs. MCP's client-server protocol letting one tool server serve any compatible model client.
Two ways to run the same model, with completely different bills, control, and blast radius. Illustrated comparison of a request traveling over the network to a hosted provider vs. inference running on infrastructure you own — the real tradeoffs in latency, cost model, data privacy, and control over weights and versions.