The Hundred-Page Machine Learning Book by Andriy Burkov - Non Fiction - Paperback
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Title:
The Hundred-Page Machine Learning Book
Condition: BRAND NEW
Format: Paperback
Overview:
Compact, readable, and relentlessly practical, The Hundred-Page Machine Learning Book is built for real-world builders who need to move from theory to implementation without wading through dense textbooks. In these pages, Andriy Burkov distills the core ideas of machine learning into approachable explanations, with clear definitions, worked examples, and bite-sized lessons you can apply on your next project. This paperback guide covers the essential algorithms and concepts that power modern data science—from supervised learning, regression, and classification, to decision trees, ensemble methods, and neural networks—along with the practicalities of training, evaluating, and deploying models. The book is intentionally concise, but not shallow: it addresses how to select models, avoid common pitfalls like overfitting and data leakage, and understand performance metrics relevant to business goals. It’s ideal for busy professionals in software, data science, product teams, and students who need a reliable, portable map of the ML landscape. The tone is friendly yet rigorous, balancing intuition with enough detail to implement techniques on real datasets. If you’re starting out or looking for a reference you’ll actually reach for, this is your first stop in the ML journey. Praised by Peter Norvig, Google's Research Director and co-author of AIMA, for distilling complex ML ideas into an accessible, actionable guide.
What Makes This Book Stand Out:
Burkov’s compact approach isn’t a cheat-sheet; it’s a carefully curated curriculum for active practitioners. The book focuses on the decisions that matter in production ML: choosing the right model for the problem, preparing data with straightforward pipelines, validating results with robust metrics, and understanding the trade-offs between bias and variance. It bridges theory and practice by delivering crisp explanations of core ideas like regularization, cross-validation, feature engineering, and transfer learning, alongside practical tips for debugging models and interpreting results. The writing invites you to think in terms of a repeatable workflow you can apply to a wide range of problems—from a recommendation engine to a churn predictor or an IoT sensor system. It prioritises real-world usefulness over theoretical exhaustiveness, making it a powerful desk reference for daily decision-making in ML projects.
Who This Book Is Perfect For:
This book is ideal for engineers, data scientists, ML engineers, product managers, and business analysts who need solid grounding quickly. It suits beginners seeking a practical entry point, as well as busy professionals needing a concise desk reference between sprints. Students in computer science, analytics, or economics will benefit from approachable explanations, while team leads can use it to align cross-functional understanding of ML workflows. Whether you’re designing a new feature, prepping for interviews, or building a portfolio to showcase your skills, this paperback delivers a trustworthy, action-oriented map of modern machine learning.
Key Highlights:
- Compact, practical coverage of core ML concepts
- Clear explanations with minimal jargon and math
- Guidance on model selection, training, and evaluation
- Production-focused insights to avoid data leakage and overfitting
- Portable paperback format for on-the-go learning
- Links theory to real-world applications for tangible results
- Praise from respected AI voices, including Peter Norvig
About the Author:
Andriy Burkov is a data scientist and educator known for his concise, practical writing on machine learning and data science. The Hundred-Page Machine Learning Book is his best-known work, celebrated for translating complex ideas into actionable knowledge that professionals can apply on real projects. Burkov emphasizes clarity, memorable frameworks, and hands-on techniques designed to help readers move from theory to deployment quickly. He writes with a focus on making ML approachable for developers, analysts, and product teams, ensuring readers gain confidence in model selection, validation, and interpretation. This book reflects his mission to make essential ML concepts accessible without oversaturation, delivering a reliable guide that busy practitioners will actually keep by their desks.
Why You’ll Love This Book:
If you’re learning on the move, building a portfolio, or guiding a team through a data-driven project, this guide delivers concrete steps, checklists, and mental models you can apply immediately. The compact, paperback format makes it a convenient desk companion you’ll annotate and flip back to as you work. It’s a thoughtful gift idea for aspiring data scientists and a practical upgrade for seasoned practitioners who want a quick, trusted refresher. Owning this single-volume guide also sets a clear learning path for your ML education and helps you communicate confidently with colleagues and stakeholders about model choices and outcomes.
Please Note: The individual books included in this listing will be dispatched as per the original UK ISBN and UK edition cover image shown in the image.
