I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
I wrote...
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
By
Yuxi (Hayden) Liu
What is my book about?
Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python. Each chapter of the book walks you through an industry adopted application. At the same time, this book provides actionable insights into the key fundamentals of ML with Python.
With the help of this extended and updated 3rd edition, you’ll understand how to tackle data-driven problems and implement your solutions with popular Python packages such as TensorFlow, PyTorch, scikit-learn, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as recommendation engine, stock price prediction with artificial neural networks, clothing categorization, sequence prediction, decision making leveraging reinforcement learning, and more. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.
By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
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The Books I Picked & Why
Machine Learning For Absolute Beginners: A Plain English Introduction
By
Oliver Theobald
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
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
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
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
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.
