Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale
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Description

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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills.

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.

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4.5 out of 54.5 out of 5
424 global ratings

Top reviews from the United States

Curtis Von Gunten
5.0 out of 5 starsVerified Purchase
Better than Geron''s "Hands-on machine learning" book for starting out in machine learning
Reviewed in the United States on May 16, 2019
Update: Geron''s book is much longer than this one (856 pages vs 400) and almost two-thirds of Geron''s is about deep learning. This review is comparing this book to the first third of Geron''s book. This book only provides several pages on deep learning (neural nets), similar... See more
Update: Geron''s book is much longer than this one (856 pages vs 400) and almost two-thirds of Geron''s is about deep learning. This review is comparing this book to the first third of Geron''s book. This book only provides several pages on deep learning (neural nets), similar to the other models. I would suggest this book for a basic foundation and if you want to dig into the minutiae of deep learning read the last 430(!) pages of Geron.

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 an 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.
59 people found this helpful
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R
2.0 out of 5 starsVerified Purchase
Average
Reviewed in the United States on June 9, 2019
The purpose of this text, to cover "all the important aspects of implementing machine learning without requiring you to take advanced math courses", is its greatest weakness. If you''re looking to obtain anything more than the most rudimentary of understandings of how the... See more
The purpose of this text, to cover "all the important aspects of implementing machine learning without requiring you to take advanced math courses", is its greatest weakness. If you''re looking to obtain anything more than the most rudimentary of understandings of how the included ML algorithms function, either mathematically or programmatically, you won''t find satisfactory explanations in this text. The problem is that some of these algorithms simply require a detailed explanation, and stripping out or barely touching on the technical details just leads to confusion.

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.
18 people found this helpful
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Amazon Customer
5.0 out of 5 starsVerified Purchase
The best Machine Learning with Python Book
Reviewed in the United States on August 20, 2019
I used this book when I was first learning Machine Learning and, years later, I still reference this book. It is well written, well organized, easy for a beginner to follow, with hands-on examples, and thorough enough to be valuable to advanced practitioners.... See more
I used this book when I was first learning Machine Learning and, years later, I still reference this book. It is well written, well organized, easy for a beginner to follow, with hands-on examples, and thorough enough to be valuable to advanced practitioners.

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.
8 people found this helpful
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Pablo F Souza
3.0 out of 5 starsVerified Purchase
Should not have been black and white !
Reviewed in the United States on August 6, 2018
The book is printed in black-and-white making it *really* hard to understand which classes / data points the authors are referring to.

Nevertheless, this is a good intro book and a nice companion to online classes that do not provide written notes.
14 people found this helpful
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Admin
1.0 out of 5 starsVerified Purchase
DID NOT Help me finally understand Machine Learning
Reviewed in the United States on December 18, 2019
Until reading this book I tried many resources to learn ML. None of them helped me grasp the concepts. This book finally made things click. Well written, with simple examples and thorough enough explanation of the fundamental concepts without overwhelming the beginner... See more
Until reading this book I tried many resources to learn ML. None of them helped me grasp the concepts. This book finally made things click. Well written, with simple examples and thorough enough explanation of the fundamental concepts without overwhelming the beginner reader. EDIT: Forget everything I wrote. Just got to the part on unsupervised learning. Now I give it a 1-star rating. Poor explanations on everything, causing me to go search for real explanations. Can anyone recommend a real machine learning book?
2 people found this helpful
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Stephen
4.0 out of 5 starsVerified Purchase
Good for theory, a little light on practical applications
Reviewed in the United States on June 17, 2020
A really great guide to Machine Learning and the theory behind some key algorithms. This book is not exactly a "cookbook". There are examples to follow and you will build models, of course, but it is more about understanding machine learning than "doing" it.
One person found this helpful
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Regular Guy
5.0 out of 5 starsVerified Purchase
Good content, terrible printing!
Reviewed in the United States on October 28, 2019
This looks to be a fine introduction to machine learning using Python. My problem is with the printing, which is smeared on many pages. The smeared printing makes many sections of the book unreadable. I have asked for another copy, and I will update my review once I have... See more
This looks to be a fine introduction to machine learning using Python. My problem is with the printing, which is smeared on many pages. The smeared printing makes many sections of the book unreadable. I have asked for another copy, and I will update my review once I have received it.

Edit 3/21/2020 Received new copy that is readable. Changing rating to reflect original opinion re: content.
2 people found this helpful
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gcgutier
5.0 out of 5 starsVerified Purchase
Exactly what I was looking for
Reviewed in the United States on November 1, 2016
Fantastic introduction to machine learning in Python. The examples are well written, and do a very nice job of introducing both the implementation and the concept for each model. I''m halfway thru the book, and am really enjoying it. I have a background in math... See more
Fantastic introduction to machine learning in Python. The examples are well written, and do a very nice job of introducing both the implementation and the concept for each model. I''m halfway thru the book, and am really enjoying it.

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.
43 people found this helpful
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Top reviews from other countries

Mike
3.0 out of 5 starsVerified Purchase
Not really that great
Reviewed in the United Kingdom on August 13, 2018
The book is meant to be introductory but dives straight into Python programming with NumPy and sklearn without showing the ropes of the libraries. The introduction to the ML concepts is gentle and well explained but the code is shoved down your throat and you better run to...See more
The book is meant to be introductory but dives straight into Python programming with NumPy and sklearn without showing the ropes of the libraries. The introduction to the ML concepts is gentle and well explained but the code is shoved down your throat and you better run to the docs to see what is actually does. 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.
8 people found this helpful
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Ben
5.0 out of 5 starsVerified Purchase
Good guide to starting ML
Reviewed in the United Kingdom on March 1, 2020
Decent guide to starting machine learning. The difficulty level is geared towards those with a little knowledge of computer science & programming, but might be too gentle for more experienced coders. I''m only part-way in, so will try to remember to update once I''ve...See more
Decent guide to starting machine learning. The difficulty level is geared towards those with a little knowledge of computer science & programming, but might be too gentle for more experienced coders. I''m only part-way in, so will try to remember to update once I''ve completed the book.
One person found this helpful
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Oya Kesgin
1.0 out of 5 starsVerified Purchase
not worth buying it
Reviewed in the United Kingdom on February 4, 2019
The explanation on machine learning is very basic and the codes inside doesn''t worth buying it. It s waste of your money.
5 people found this helpful
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Paulo
4.0 out of 5 starsVerified Purchase
Lear how to apply ML DL techniques to datasets.
Reviewed in the United Kingdom on May 1, 2021
Great summary of open source available ML DL techniques. Various algos employed, detailed, explained. Perfect to start building skills on these topics. Great accessory if you are teaching yourself online.
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Mr. J. A. Bravo
5.0 out of 5 starsVerified Purchase
Better then browsing endlessly
Reviewed in the United Kingdom on August 28, 2018
Good structured book ideal for learning ML.
One person found this helpful
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Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale

Introduction outlet sale to 2021 Machine Learning with Python: A Guide for Data Scientists sale