Learn to design, build, and ship real AI systems — from ML/deep learning fundamentals through transformers and LLMs, prompt engineering, agents and multi-agent systems, RAG, fine-tuning, deployment, and AI safety.
25 modules in 7 phases, with worked examples, interactive tools, labs and a quiz in every one, and a certificate of completion.
Compare model results and document an evaluation decision.
Help-center articles are not uniform prose; they are structured documents with headings, numbered steps, and warning callouts, and a chunking strategy that ignores that structure will routinely cut a numbered procedure in half — step 3 lands in one chunk, step 4 in the next, and a retrieval that only surfaces the chunk containing step 3 hands the model an instruction that references "the setting you changed above"…
Selected training excerpt, not a complete design or a project submission. The full project includes its assumptions, methods and checks.
Build your own phase-by-phase workbook, record the evidence behind your decisions and download your work.
Review a set of flagged cases. Change the number of correct detections and false alarms to compare precision.
Precision = true positives ÷ (true positives + false positives) × 100
A small illustrative evaluation set; production decisions also need recall, representative data and cost/risk analysis. This free exercise does not mark a paid module complete.
Related full-program tool: AI Agent lab →Choose a phase to explore its lessons and project work.
Start with the free lesson →Covers AI Fundamentals, Machine Learning and Deep Learning.
How AI, machine learning, and deep learning nest, narrow vs. general AI, and the AI engineering lifecycle.
Supervised vs. unsupervised learning, train/val/test splits, overfitting, classical algorithms, and evaluation metrics.
Neurons, activation functions, backpropagation and gradient descent, CNNs, and RNN/LSTM limitations.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
Covers Transformers, Large Language Models and Prompt Engineering.
Self-attention, positional encoding, multi-head attention, and the encoder-decoder vs. decoder-only distinction.
Scaling laws, pretraining, context windows, and the SFT/RLHF alignment stages that make a model helpful.
Zero/few-shot prompting, chain-of-thought, system vs. user prompts, sampling parameters, and prompt evaluation.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
Covers AI Agents, Multi-Agent Systems, RAG and Fine-Tuning.
Tool/function calling, the ReAct loop, MCP, and agent memory.
Task decomposition, orchestrator/subagent topologies, and the LangGraph/CrewAI framework landscape.
Chunking, embeddings, retrieval and re-ranking, and why grounding reduces but never eliminates hallucination.
When to fine-tune vs. use RAG or prompting, LoRA/QLoRA, RLHF/DPO, and dataset quality.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
Covers AI Hardware, Model Deployment, AI System Architecture and AI Safety.
GPUs/TPUs/NPUs, CUDA and the software stack, memory bandwidth, and edge AI constraints.
API vs. self-hosted, quantization, KV-caching, batching, and latency-vs-throughput tradeoffs.
The 8-stage AI lifecycle, the seven core AI engineering disciplines, and designing for AI-specific failure.
Hallucination, prompt injection, bias, guardrails, human-in-the-loop design, and the EU AI Act.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
Covers Engineering Applications, AI Automation and Troubleshooting.
Computer vision inspection, CAD/BIM copilots, and AI for NEC calculations, protection coordination, and PV sizing.
No-code automation with n8n/Make, the AI Agent node, and when to graduate to custom code.
Real diagnostic scenarios — retrieval failures, rate-limit degradation, agent tool loops, and eval/production drift.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
You apply what you have learned to 5 complete designs, starting with Customer Support RAG Chatbot.
A grounded help-center chatbot — chunking strategy, vector-index sizing, and a retrieval + re-ranking pipeline.
An orchestrator + researcher subagents + writer agent — task decomposition and a worked cost/latency calculation.
LoRA fine-tuning vs. a general LLM call — a worked cost comparison and asymmetric precision/recall tradeoffs.
Self-hosted vs. API — a worked cost-crossover calculation and a latency/throughput budget.
A CV triage pipeline for inspection photos — precision/recall tradeoffs and a human-in-the-loop review workflow.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
Covers Professional Engineering Practice, Documentation & Resource Kit and Capstone: AI Assistant Design & Certification.
Requirements definition, AI-specific design reviews, model cards, vendor evaluation, and versioning discipline.
A downloadable kit of 12 real project templates, from a feasibility worksheet to a project handoff runbook.
A final internal-knowledge-assistant design assignment, drawing-interpretation and troubleshooting exercises, and your certificate of completion.
Project checkpoint: Record the assumptions, calculations and evidence behind this phase’s outcome in your project workbook.
By the end: Covers AI Fundamentals, Machine Learning and Deep Learning.
AI system evaluation workbook: Compare model results and document an evaluation decision. Keep a result and a verification note for each phase.
Your All-Access Pass includes the project workbook, lesson notes, self-review checklists and portfolio downloads. Try the free lesson above to see a worked example.
Your name
Applied AI Engineering Professional ProgramEngineersUniverse · self-paced learning
Finish all required module quizzes to unlock your certificate of completion. Modules with a score requirement must meet that requirement. Your project portfolio and skills summary are separate downloads for your own learning record.
A certificate of completion is not a professional license or accreditation.
12 real project templates — a model & use-case feasibility worksheet, a prompt design & evaluation worksheet, a RAG pipeline design worksheet, a fine-tuning dataset & hyperparameter worksheet, a model evaluation & benchmark report template, an AI system architecture review checklist, an AI safety & risk assessment checklist, a deployment & inference cost worksheet, a monitoring & observability checklist, an AI debug & incident log template, a data governance & privacy checklist, and a project handoff & runbook template. Available for download from Module 20 (Documentation & Resource Kit) once you've unlocked the program.