Machine Learning Intuition
Preface
The Project
Machine Learning and AI have taken our world by storm. And yet, so few people truly understand these technologies.
I have spent a large part of my career solving practical problems with Machine Learning. This book is my attempt to explain the intuition behind those solutions for family, friends and curious readers.
If you want to understand how an algorithm can learn from examples and make useful predictions, welcome! You are in the right place. Expect clear explanations and simple visualisations rather than heavy mathematics.
The first part is a self-contained introduction to prediction with Machine Learning. It explains the core ideas you need to make sense of models that learn from data.
The following chapters explore important aspects of Machine Learning practice — for example: modelling approaches, model evaluation, and data preprocessing.
The final section brings these pieces together with an accessible end-to-end Machine Learning project.
The last chapter reflects on how modern generative AI models connect with the ideas presented here.
About Me
I am an Applied Science Manager and Computer Science Lecturer in Berlin. I have a blog and newsletter, subscribe here to read my latest work.
Notes
- Intuition first: the book focuses on intuition and real-world thinking rather than formal proofs. If you would like a more code-heavy or mathematical approach, please get in touch.
- Synthetic data: all examples use synthetic data for clarity. Nothing in this book is medical, legal or financial advice.