Available Courses

Build a personal AI assistant that connects to your real tools and data — from first API call to production-ready system.

Go inside the black box. Learn how machine learning actually works - the algorithms, the tradeoffs, and the decisions that turn data into predictions.

What is intelligence — and what happens when you build it into a machine? This course starts at the beginning. Not with code or tools, but with the question of what intelligence actually is: the ability to recognise differences, similarities, and identities, and to perceive, pose, and resolve problems. Humans do this naturally. AI does it computationally. Understanding the connection between the two is the foundation for everything else. From there, you'll learn how machines learn from data — the algorithms, datasets, and patterns that make it work. You'll see the three main approaches to machine learning (supervised, unsupervised, and reinforcement learning) and understand why each exists. You'll discover how neural networks and deep learning triggered the AI revolution we're living through. You'll explore how AI processes the world — understanding language through natural language processing and interpreting images through computer vision. Then you'll meet the technology behind the current boom: generative AI, large language models, and the chatbots that have put AI in everyone's hands. The course closes where it must: with the real-world applications of AI, the biases it can inherit, and the ethical responsibility that comes with building systems that make decisions affecting people's lives. No prior technical knowledge is needed. Just curiosity about how the most transformative technology of our time actually works.

Thinking is a skill. Like any skill, it can be learned, practised, and improved. Every day you take in data, form opinions, and make decisions. But how do you know if the data is good? How do you tell a fact from an opinion? How do you spot when something doesn't make sense — and figure out how to make it right? This course gives you the tools of clear thinking. You'll start with the basics — what a thought is, what data is, and the three mental skills that make rational thinking possible: seeing differences, seeing similarities, and seeing when things are identical. You'll learn to sort data into categories — facts, opinions, laws, orders, and suggestions — so you always know what you're working with. Then you'll learn the five primary illogics: the five fundamental ways things go wrong. Missing data. Wrong sequence. Dropped time. Falsehood. Altered importance. Once you can spot these, you'll see them everywhere — in news stories, in workplace problems, in everyday confusion. For each illogic, there's a matching logic — the way things should be. You'll learn all five, and then use them as tools: outpoints (signs that something is wrong) and pluspoints (signs that something is right). Finally, you'll expand to the full set — fourteen outpoints and fourteen pluspoints — giving you a complete toolkit for evaluating any situation, any report, any plan. By the end, you won't just think more clearly. You'll have a precise, practical system for finding what's wrong, confirming what's right, and making better decisions in any area of life.

Applied AI engineering is about building software with language models — not training them. You won't write ML pipelines or tune hyperparameters. You'll learn how to communicate with models, integrate them into real systems, and ship them to production. This course teaches the foundational concepts every applied AI engineer needs. You'll learn what tokens are and why they matter, how context windows shape every architectural decision, and how to communicate effectively with models through prompts, structured output, and chain of thought reasoning. You'll move from communication to integration — connecting models to real data through grounding and RAG, extending their capabilities with tool use, and making integrations portable with the Model Context Protocol (MCP). Then you'll build on these primitives to understand agents: AI systems that reason, plan, and act autonomously. You'll learn the agent loop, specification-driven development, and how to compose agents from reusable skills. Finally, you'll learn the practices that separate prototypes from production: evaluation (measuring what matters) and guardrails (keeping AI systems safe and reliable). By the end, you'll have a clear mental model for every core concept in applied AI engineering — and the vocabulary to go deeper on any of them.

This course gives you a deep, practical understanding of Trigonometry - not just enough to pass an exam, but enough to make it part of how you think. You'll learn the fundamental principles in a way that sticks, so you can apply them with confidence long after the course is over.