Why This Course?
Artificial Intelligence is everywhere, but most introductions to AI focus only on using tools like ChatGPT rather than understanding how they work. Many beginner courses teach AI as if it’s just "a smart algorithm" or "a system that predicts things," without explaining the core concepts that make AI possible.
I believe that true understanding comes from learning why AI systems work, not just how to use them. That’s why this course goes beyond prompt engineering and buzzwords. It provides a solid foundation in the mathematical and conceptual building blocks of AI, such as probability, derivatives, and distributions, with intuitive examples.
This course breaks AI into bite-sized lessons that aims to help learners of all backgrounds grasp essential principles in an engaging way. Whether you’re curious about how AI models learn, why probability matters in machine learning, or what makes neural networks effective, this course will give you the insights you need to truly understand AI—not just use it.
Working alone on this course, I've relied on AI assistance for both content creation and translation. I apologize if this is overly apparent in the writing.
About Me
I’m a Master’s student in Artificial Intelligence at the University of Amsterdam (UvA) and work part-time at the Amsterdam University of Applied Sciences (HvA), where I help integrate AI into education. I give workshops, assist in AI-driven projects like chatbots and document analysis, and explore ways to make AI more accessible.
I created AI for Dummies as a resource for my workshops and for anyone interested in understanding AI at a deeper level. I noticed that many beginner courses focus only on applications, without explaining the core principles. But AI is more than just prompts—it’s built on math, logic, and structured problem-solving. My goal is to bridge the gap between simple AI explanations and the technical knowledge that makes AI truly work.
If you'd like to get in touch or see more of my work, checkout my personal website.