I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL.
I wrote...
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. We catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
There are three types of machine learning books — books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.
If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you want to learn them in a practical way. You need a book that leads with intuition and shows you implementations with code. It has to do this without getting sidetracked into ML theory, getting mired in statistical concepts, or being so superficial that you don’t understand why the code works. Aurélien gracefully threads this needle — that’s what makes his book so good.
This is a very clearly written book. The author uses a simple framework (scikit-learn) to explain the basics, and then moves to TensorFlow for more realistic examples. Throughout, the book is immensely pragmatic. I strongly recommend this as your first ML book.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help…
General-purpose machine learning is not why machine learning is so popular. Instead, ML is popular because a branch of ML, called deep learning, has proven to be incredibly powerful at handling unstructured data — images, video, natural language text, audio/speech, etc.
Francois Chollet is the author of Keras, the leading software framework for deep learning. As with Aurelien’s book, Francois’ book is clearly written, immensely pragmatic, and will give you the necessary intuition and show you how to implement deep learning models in code.
"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook
The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher Francois Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning…
This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.
Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.
The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing images or time series, read the corresponding books in the same series instead.
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.
Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to…
The difference between an ML beginner and an ML expert is that the ML expert doesn’t try to build something that they can simply reuse. But the expert also has the judgment to recognize scenarios where it is worth building something — this is usually because the current, generic, state-of-the-art (SoTA) models won’t be good enough.
Jeremy shows you what the state of the art (SoTA) looks like across a wide variety of ML fields, and how to use SoTA models to get what you need. If the first three books will make you a good ML engineer, this book will help you inch your way towards knowing what ML researchers know.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.
Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to…
Even if you have the practical knowledge, it's sometimes necessary to understand the mathematical and theoretical concepts that underlie the machine learning approaches you are using. This book is a great introduction to the world of ML theory.
WARNING: will not work on e-ink Kindle devices! Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."
Aurélien Géron, Senior AI Engineer, author of the…
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