AI Trainer in Dubai —
The One Who Has Actually Shipped AI

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

This is the track where you stop nodding along to AI buzzwords. It's a technical overview with hands-on labs to make the concepts real — enough to genuinely understand how ML, deep learning, LLMs, RAG and agents work, without having to become a full-time ML engineer. And because I've built and broken these systems, you learn what actually 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 in AI · transparency and explainability · data security · risk management · the anatomy of a decision · asking the right questions and defining 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 do not need to code. They need to spot the right AI bets, kill the wrong ones, and not get sold snake oil. That is what this track is built to do.

International AI standards and frameworks · AI laws and regulations worldwide · understanding 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 track connects straight to AI governance and ISO/IEC 42001 — the management-system standard for governing AI. I am an ISO/IEC 42001 Senior Lead Implementer, so I teach risk and governance the way an implementer does: to survive an audit, not to pass a quiz. Full ISO 42001 course detail: iso42001.ae ↗.