Supervised Learning from First Principles

Six months ago, I knew nothing about Machine Learning since I had always viewed Machine Learning as a distant, almost mystical field reserved for mathematicians and data scientists. Then one day, curiosity got the better of me. I wanted to know, how does a machine actually learn?

That simple question sent me down a rabbit hole. I read countless books, watched hours of videos, and wrestled with concepts that stretched my understanding of math, statistics, and algorithms. The journey was humbling. There were moments when I felt utterly lost, and others when a concept finally clicked and lit up my brain like a light bulb.

This book was born out of that experience: the struggle, the joy, and the sheer curiosity that kept me going. It's written with pure love for understanding how the hell machine learning actually works from the ground up. Note that I'm not an expert in Machine Learning or Artificial Intelligence and by the end of this book, you won't be one either. That's not the goal. What this book aims to do is something more fundamental: to help you see a drop from the ocean of ML knowledge through the lens of first principles, much like understanding physics through its equations. By the time you finish, you'll have the intuition, mathematical grounding, and confidence to explore more advanced algorithms and concepts on your own, but with true understanding. We'll explore the underlying mathematics, visualize concepts through graphs, derive algorithms step by step, and eventually bring them to life with Python.

The goal is not to rush to the latest buzzwords or neural networks, but to peek under the hood of machine learning to see how equations turn into intelligent behaviour.