How to Read This Book¶
This is a set of interactive Jupyter notebooks. Start here, then open the Contents page to choose each chapter in order. At the end of every chapter you'll find a What's Next note pointing to the next one.
- Recommended: read the chapters in order (1 → 11), because later chapters build on earlier ones.
- Every code cell has already been run, so you can read the outputs (plots and printed results) without running anything yourself.
- To re-run a notebook yourself: open it, then Kernel → Restart & Run All. All required libraries (
numpy,pandas,scikit-learn,matplotlib,scipy, and a littletensorflowin Chapter 6) are already installed in your Anaconda environment.
Preface¶
This book is a set of interactive Jupyter notebooks that re-teaches the ideas in Andriy Burkov's The Hundred-Page Machine Learning Book in a slower, friendlier, hands-on way. Burkov's book is famous for packing the practical core of machine learning into about a hundred pages. This companion unpacks those pages into explanations you can read at a relaxed pace, with runnable Python in every chapter so you can see each idea in action rather than just read about it.
Who this is for. Beginners who want a clear, intuitive first tour of machine learning, and practitioners who want a quick refresher with code. No deep math background is required — we introduce notation gently and always pair formulas with examples.
A note on authorship. The original ideas, structure, and technical content come from Andriy Burkov's book. This companion was adapted and edited for clarity and interactivity; any errors or awkward explanations in the adaptation are mine.
— [Your Name]
Contents
Open the Contents page to browse and read each chapter.
How These Notebooks Are Organized¶
- Each chapter is its own
.ipynbfile and is self-contained — you can read chapters independently, though they build on each other in order. - Every notebook starts with an "In this chapter you will learn" list and ends with Key Takeaways and a pointer to the next chapter.
- Python cells use small, synthetic datasets so they run in seconds on any laptop, with no internet or large downloads required.
- Math is kept light: we explain symbols in words before using them, and pair every formula with a concrete example.
Copyright¶
© 2026 IRays-Teknology-Ltd. All rights reserved.
First technical edition, September 2026.
List price: $9.99 USD (print paperback). ISBN: 978-0-0000000-0-0 (placeholder — assign your own before publishing).
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, contact the publisher at IRays-Teknology-Ltd.
A Note on This Work¶
This book is an interactive companion that re-explains, in simpler language with runnable Python examples, the ideas in Andriy Burkov's The Hundred-Page Machine Learning Book (2019). The original concepts, structure, and technical content are attributed to Burkov; this edition was adapted and edited by Miftahur Rahman, Ph.D. Readers are advised to consult the original work for the author's full treatment. The Python examples and pedagogical explanations are original to this companion.
Disclaimer¶
The information in this book is provided "as is" for educational purposes. While every effort has been made to ensure accuracy, neither the publisher nor the editor warrants the correctness of all examples and accepts no liability for any use thereof.