Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition
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About the Author
Andreas Müller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
- Publisher : O'Reilly Media; 1st edition (November 1, 2016)
- Language : English
- Paperback : 400 pages
- ISBN-10 : 1449369413
- ISBN-13 : 978-1449369415
- Item Weight : 1.3 pounds
- Dimensions : 7 x 0.82 x 9.19 inches
- Best Sellers Rank: #24,618 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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I read the Geron book "Hands-on Machine Learning with Scikit-learn & TensorFlow" before reading this book. (Note: I am not reviewing the TensorFlow and Keras sections). This book provides a better start for several reasons. First, this book is better organized. Second, the code implementations rely primarily on Python modules, instead of custom programming.
Regarding the first, this book is set-up so that a reader can get an understanding of Machine Learning (ML) step-by-step from the bottom-up. For instance, supervised learning, feature engineering, and model evaluation all get separate chapters. The model evaluation chapter provides and entire section, as well as graphics, for understanding the roles of training, validation, and test data, which are probably the most important bedrock concepts in ML. In contrast to this, Geron throws you right into an entire ML pipeline in the second chapter. It's a mix of feature engineering, linear models, stochastic gradient descent, random forest models, cross-validation, grid search, and even object oriented programming for custom transformers! This might be useful for quickly understanding what ML is like in practice. If later sections of Geron then went step-by-step and elaborated on the second chapter, it would be great. Instead, for instance, the second chapter is randomly about binary classification for picture data. You literally only get two paragraphs in the first chapter on cross-validation and validation sets, and a sentence or two later in the book. I had to go to Wikipedia to ensure that I understood it correctly and robustly. I wish I had read this book instead.
Regarding the second, this book does not assume a heavy programming background. Most of the ML pipeline is taught through the Python module Scikit-Learn. This is useful because the programming does not distract from learning fundamentals of ML. In contrast, in the second chapter of Geron, there is object oriented programming code involving concepts like constructors and inheritance. For this book, the most sophisticated chapter at the end, which is on pipelines and which expertly explains why feature engineering should be performed during model evaluation, doesn't even go into this.
In summary Geron teaches more advanced topics interspersed with the basics without a coherent organizational structure. This book has an intuitive structure that elaborates at length on core ML concepts and doesn't overburden with moderate-to-complex programming.
Another issue is the mglearn library that is required for this text. It is a huge annoyance because it obscures code that is otherwise necessary to understand if you have any intention of transferring the information in this text to the real world.
Some general concepts are explained well, but clarity begins to decline as topics become more complex. Almost all the code is poorly explained. Expect to spend as much time, if not more, examining the documentation for the referenced libraries as you will reading this text if you hope to get anything useful out of it.
This book shows you how to use the various machine learning algorithms, and provides an intuitive discussion of how they work, but it does not go into the mathematical details needed to program the algorithms from scratch. Thus, this book is perfect for the practitioner, but does not attempt to teach the theory or mathematics behind the algorithms.
Nevertheless, this is a good intro book and a nice companion to online classes that do not provide written notes.
I have a background in math and wrote software professionally for a number of years, but haven't spent much time doing either for the past 5-10 years. This book is technical enough to keep me interested, and accessible enough to allow me to ramp up on the language and the scikit framework.
An added bonus - the instructions actually allowed me to set up my development environment, and the code in the book actually runs!
100% recommend for someone looking to get started in ML with Python.
Edit 3/21/2020 Received new copy that is readable. Changing rating to reflect original opinion re: content.
Top reviews from other countries
Saving point is: if you are teaching ML (like me) and need good well designed examples go for this book; also if you need very visual explanations. Would not recommend the book for a student though.
Various algos employed, detailed, explained.
Perfect to start building skills on these topics. Great accessory if you are teaching yourself online.