AI Trainer in the UAE —
Practitioner-Led, Nationwide

GenAI Fluency is a practitioner-led, zero-to-fluent programme that leaves you understanding the models, the vocabulary, and the fast-moving landscape — and able to keep up on your own long after the buzzwords change. Seven modules and a hands-on capstone:

• Where GenAI fits — how generative AI sits inside the wider AI family, and the whole story from transformers to ChatGPT to agents.
• The vocabulary, by intuition — token, LLM, context window, parameters, temperature, hallucination, embeddings, RAG, fine-tuning, multimodal, agents, and MCP — demystified by intuition rather than jargon.
• The landscape — the frontier labs and open-weight players, how to read a model card, and how to see through benchmarks. Refreshed live every cohort, because it changes weekly.
• How it actually works — just enough of the machinery to know why these systems predict, why they hallucinate, and why they fumble at math.
• What you can genuinely do — real capabilities across text, image, audio, video, and code, including RAG and the agents-plus-MCP frontier of connecting AI to your own systems.
• The limits, honestly — hallucination, bias, data leakage, and shadow AI, with a simple framework for when to trust the output and when not to.
• Judgment and model selection — telling a real capability from a marketing claim, and picking and routing the right model for the job.
• Capstone — a hands-on project that pulls it all together.

You walk out able to: speak the language fluently, tell a real capability from a marketing claim, pick and route the right model for a task, and judge when GenAI belongs in that task at all.

Not everyone needs to build AI — but everyone needs to use it well and not get burned by it. What sets this apart: it's taught by someone who actually builds and governs these systems, it's designed to keep you current rather than to memorise today's tools, and it ends where serious organisations go next — not just using GenAI, but governing it. That makes it the natural on-ramp to the AI risk and governance track below, and to ISO/IEC 42001 on iso42001.ae ↗.

Fundamental concepts and principles of AI · Python for AI (hands-on lab) · data analysis and visualization (hands-on lab) · foundations of data science and machine learning · the ML workflow · supervised learning · unsupervised learning · advanced ML and applications (lab) · foundations of deep learning · core deep learning architectures · generative architectures · computer vision · deep learning in applied domains (lab) · natural language processing · large language models · Retrieval-Augmented Generation (RAG) · agentic AI systems · AI security · AI ethics · AI governance and strategy.

This is where you stop nodding along to AI buzzwords. A technical overview with hands-on labs to make it real — enough to genuinely understand how ML, deep learning, LLMs, RAG and agents work, without becoming a full-time ML engineer. Because I've built and broken these systems, you learn what matters versus what's hype.

AI fundamentals · AI capabilities and global trends · strategic value and business alignment · identifying and prioritizing AI opportunities · data readiness and organizational foundations · managing AI projects · AI governance · AI policy · fairness and bias · transparency and explainability · data security · risk management · the anatomy of a decision · defining the right questions and KPIs · understanding data and business patterns · data storytelling · hands-on Power BI · GenAI and LLM fundamentals · architectures, agents, and agentic systems · data, knowledge, and RAG · the AI automation project life cycle · hands-on automation with n8n.

Leaders don't need to code. They need to pick the right AI bets and kill the wrong ones. That's this track.

International AI standards and frameworks · AI laws and regulations worldwide · risks, impacts, and harms · the AI life cycle · standing up an AI risk-management program · organizational context and AI risk governance · AI risk mapping and identification · AI risk analysis · AI risk evaluation and prioritization · AI risk treatment · AI risk monitoring and reporting · training and awareness · optimizing and evaluating AI risk performance.

This connects straight to AI governance and ISO/IEC 42001. I'm an ISO/IEC 42001 Senior Lead Implementer, so I teach governance to survive an audit — not to pass a quiz. Full ISO 42001 detail: iso42001.ae ↗.