AI learning paths
Structured paths to go from where you are to working in AI — each one built around real projects, not a reading list.
Prompt Engineering
Get reliable output from AI models — on purpose, not by luck.
AI for Leaders
Decide what to build, what to buy, and what to refuse — without writing a line of code.
AI Automation
Automate real work with little or no code — and keep it running after you leave the room.
LLM Applications
Close the gap between a demo that impresses and a product that holds.
Generative Media
Image, video and audio you can actually deliver — because a brief is not a lottery ticket.
AI-Augmented Data Analyst
Answer the question that was actually asked — and know when your answer is wrong.
AI Agents
Systems that decide and act — and fail gracefully when they decide wrong.
Machine Learning
From a messy table to a model you can defend in a meeting.
Deep Learning
Understand neural networks well enough to debug them at 2am.
NLP & Transformers
Understand how machines read language — and why they read Arabic badly.
Computer Vision
From pixels to a camera that works in a real, badly lit room.
MLOps
Put AI in production and keep it there — through drift, incidents and invoices.
How to use these paths
Pick the path that matches where you want to end up, not the one that sounds most impressive.
Follow the phases in order — each one assumes the previous.
Build every project. A path you only read is a path you did not do.
Expect the timings to slip. They assume steady weekly effort, not a sprint.
Want to walk one of these paths with a coach and a cohort?
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