The best books about machine learning for beginners

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
By Yuxi (Hayden) Liu

The Books I Picked & Why

Machine Learning For Absolute Beginners: A Plain English Introduction

By Oliver Theobald

Book cover of Machine Learning For Absolute Beginners: A Plain English Introduction

Why this book?

This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you. 


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Mathematics for Machine Learning

By Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Book cover of Mathematics for Machine Learning

Why this book?

The book is a well-curated collection of the essential mathematical concepts that form ML. You may experience a cultural shock jumping to this book from the previous one, because the writing in this book is a bit formal. However, it is the missing but necessary piece for building solid foundations for practical ML. You will find it more valuable combining the intuition behind ML that you gained previously. And the explanations in the book are succinct and from the ML perspectives. For instance, partial derivatives are explained in terms of neural network weight optimization. I wish the concepts in Linear Algebra, Vector Calculus, and Probability courses back in college were introduced this way so I understand better how they are applied.  


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Introduction to Machine Learning with Python: A Guide for Data Scientists

By Andreas C. Müller, Sarah Guido

Book cover of Introduction to Machine Learning with Python: A Guide for Data Scientists

Why this book?

This book is more advanced than the first book I recommended. It presents ML theoretical and practical aspects step-by-step from the bottom up. Each chapter elaborates at length on a core building block in the ML life cycle. For example, feature engineering, supervised learning, and model evaluation have their own separate chapters, with intuitive discussions of how they work. Most of the concept is taught through the simple yet powerful Python Module Scikit-Learn so it won’t overburden you with heavy programming. This book will be perfect for practitioners with some understanding of statistics and linear algebra.


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Programming Collective Intelligence: Building Smart Web 2.0 Applications

By Toby Segaran

Book cover of Programming Collective Intelligence: Building Smart Web 2.0 Applications

Why this book?

This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.

Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.


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Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies

By John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

Book cover of Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies

Why this book?

Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.


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