🎓 Professional Training Program✨ Premium Content

Applied AI Engineering Professional Program

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.

25 Modules7 PhasesInteractive lessons5 Real Projects12-Template KitQuizzesCertificate

The big picture

Follow the arrows from the top, or jump straight into any module. Each box shows the labs, calculators and drawings you will use.

Program

25 modules in 7 phases

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Phase

a group of modules with one outcome

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Module

one unit of the program, numbered in order

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Topic

a part of a module

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Step

one screen inside a module

▶ START HERE

Foundations

Covers AI Fundamentals, Machine Learning and Deep Learning.

0/3 done

🤖Module 1🔒

AI Fundamentals

How AI, machine learning, and deep learning nest, narrow vs. general AI, and the AI engineering lifecycle.

🧮 1🔬 1

📊Module 2🔒

Machine Learning

Supervised vs. unsupervised learning, train/val/test splits, overfitting, classical algorithms, and evaluation metrics.

🕸️Module 3🔒

Deep Learning

Neurons, activation functions, backpropagation and gradient descent, CNNs, and RNN/LSTM limitations.

Language Models

Covers Transformers, Large Language Models and Prompt Engineering.

0/3 done

🔀Module 4🔒

Transformers

Self-attention, positional encoding, multi-head attention, and the encoder-decoder vs. decoder-only distinction.

🔬 1

🧠Module 5🔒

Large Language Models

Scaling laws, pretraining, context windows, and the SFT/RLHF alignment stages that make a model helpful.

📄 1🔬 1

💬Module 6🔒

Prompt Engineering

Zero/few-shot prompting, chain-of-thought, system vs. user prompts, sampling parameters, and prompt evaluation.

📄 1

Agents & Retrieval

Covers AI Agents, Multi-Agent Systems, RAG and Fine-Tuning.

0/4 done

🕹️Module 7🔒

AI Agents

Tool/function calling, the ReAct loop, MCP, and agent memory.

🔬 1📄 1🧮 1

🧑‍🤝‍🧑Module 8🔒

Multi-Agent Systems

Task decomposition, orchestrator/subagent topologies, and the LangGraph/CrewAI framework landscape.

📄 1🔬 1

📚Module 9🔒

RAG

Chunking, embeddings, retrieval and re-ranking, and why grounding reduces but never eliminates hallucination.

🎯Module 10🔒

Fine-Tuning

When to fine-tune vs. use RAG or prompting, LoRA/QLoRA, RLHF/DPO, and dataset quality.

📄 1

Infrastructure & Systems

Covers AI Hardware, Model Deployment, AI System Architecture and AI Safety.

0/4 done

💻Module 11🔒

AI Hardware

GPUs/TPUs/NPUs, CUDA and the software stack, memory bandwidth, and edge AI constraints.

🧮 1🔬 1

🚀Module 12🔒

Model Deployment

API vs. self-hosted, quantization, KV-caching, batching, and latency-vs-throughput tradeoffs.

📄 1🔬 1

🏗️Module 13🔒

AI System Architecture

The 8-stage AI lifecycle, the seven core AI engineering disciplines, and designing for AI-specific failure.

🧮 1🔬 1

🛡️Module 14🔒

AI Safety

Hallucination, prompt injection, bias, guardrails, human-in-the-loop design, and the EU AI Act.

🧮 1🔬 1

Applying It

Covers Engineering Applications, AI Automation and Troubleshooting.

0/3 done

⚙️Module 15🔒

Engineering Applications

Computer vision inspection, CAD/BIM copilots, and AI for NEC calculations, protection coordination, and PV sizing.

🔁Module 16🔒

AI Automation

No-code automation with n8n/Make, the AI Agent node, and when to graduate to custom code.

🔬 1🧮 1

🔧Module 17🔒

Troubleshooting

Real diagnostic scenarios — retrieval failures, rate-limit degradation, agent tool loops, and eval/production drift.

Real Projects

You apply what you have learned to 5 complete designs, starting with Customer Support RAG Chatbot.

0/5 done

💬Module 18🔒

Customer Support RAG Chatbot

A grounded help-center chatbot — chunking strategy, vector-index sizing, and a retrieval + re-ranking pipeline.

📝Module 19🔒

Multi-Agent Research & Report Assistant

An orchestrator + researcher subagents + writer agent — task decomposition and a worked cost/latency calculation.

📄 1🔬 1

🏷️Module 20🔒

Fine-Tuned Support-Ticket Classifier

LoRA fine-tuning vs. a general LLM call — a worked cost comparison and asymmetric precision/recall tradeoffs.

📄 1

⚡Module 21🔒

Production LLM Inference Service

Self-hosted vs. API — a worked cost-crossover calculation and a latency/throughput budget.

📄 1🔬 1

🔍Module 22🔒

AI-Powered Engineering Defect Detection Pipeline

A CV triage pipeline for inspection photos — precision/recall tradeoffs and a human-in-the-loop review workflow.

🧮 1🔬 1

Resources & Certification

Covers Professional Engineering Practice, Documentation & Resource Kit and Capstone: AI Assistant Design & Certification.

0/3 done

📑Module 23🔒

Professional Engineering Practice

Requirements definition, AI-specific design reviews, model cards, vendor evaluation, and versioning discipline.

📄Module 24🔒

Documentation & Resource Kit

A downloadable kit of 12 real project templates, from a feasibility worksheet to a project handoff runbook.

🎓Module 25🔒

Capstone: AI Assistant Design & Certification

A final internal-knowledge-assistant design assignment, drawing-interpretation and troubleshooting exercises, and your certificate of completion.

🎓 Certificate of completion

Phase by phase

Your training track

25 modules in 7 phases

Preview the path. Everything is unlocked with your purchase. Each step mixes short lessons, interactive tools, labs and practice.

Start: AI Fundamentals →

🎓Certificate

Phase 1

Foundations

By the end: Covers AI Fundamentals, Machine Learning and Deep Learning.

AI FundamentalsMachine LearningDeep Learning

🧮 Calculator🔬 Lab🎛️ Simulator🖼️ Drawings📐 Designer📄 Article🧩 Planned, not built yet

What's in the downloadable kit?

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.