Python Machine Learning, Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow , Second Edition

Sebastian Raschka

Vahid Mirjalili

$43.99

4.3 (89 Ratings)

Paperback Sep 2017 622 pages 2nd Edition

Sebastian Raschka

Vahid Mirjalili

$43.99

4.3 (89 Ratings)

Paperback Sep 2017 622 pages 2nd Edition

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Key benefits

Second edition of the bestselling book on Machine Learning
A practical approach to key frameworks in data science, machine learning, and deep learning
Use the most powerful Python libraries to implement machine learning and deep learning
Get to know the best practices to improve and optimize your machine learning systems and algorithms

Description

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published. Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities. If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

Who is this book for?

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.

What you will learn

Understand the key frameworks in data science, machine learning, and deep learning
Harness the power of the latest Python open source libraries in machine learning
Explore machine learning techniques using challenging real-world data
Master deep neural network implementation using the TensorFlow 1.x library
Learn the mechanics of classification algorithms to implement the best tool for the job
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Delve deeper into textual and social media data using sentiment analysis

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Amazon verified reviews

VISA Nov 30, 2018

Great book! Presenting the machine learning algorithms and some of the elements of the linked theory, altogether with Python code is really useful.

Amazon Verified review

sipy Aug 14, 2018

This book will stay on your reference shelf for years to come!The authors clearly have taught these materials many times before, and their significant mathematical and technical prowess is delivered using a very approachable style. This book seems best suited for someone who wants to sit down and begin to apply Python Machine Learning to a problem that they already know they have. It's not particularly an "intro course to M.L.", but it contains enough details that you could easily follow along and learn how to use the various tools and techniques of the field if you've never seen or heard of them before.The copious notes scattered throughout this book are pure gold, mined from the obvious experiences of the authors while working in the field. If there ever is a Machine Learning equivalent to the venerable "Forrest M. Mims Engineering Notebook" for electronics, I feel these two authors could write it!Once you use this book to work on your current M.L. problem in Python, you will find yourself returning to it as a reference for other problems in the M.L. space. Its lucid explanations will help reinforce the topics presented, and cement your understanding of the materials.This book will get you writing Python Machine Learning code to work your current M.L. problem in no time flat!

Dave May 20, 2018

It is the best book on Machine Learning using python. Algorithms are well explained. Suitable for beginners though you need to know some calculus.

Bio620 Mar 17, 2018

Very interesting book

Jano Aug 08, 2018

Very steep learning curve.I almost gave up in chapter two at perceptron but since that algorithm is the foundation of all I spent a whole week to understand it. The code the author uses is pretty much optimized and it was not in sync with the mathematical introduction. But the first 30 pages are absolutely neccessary to read and understand deeply in order to move on.After page 30 it became a little faster to proceed with the book since topics from page 30 - 107 are mostly the extension of the perceptron. At page 107- 160 I am already accustomedto the authors style and to the books logic so it is now quite effective to read and digest the models.And that is where I am at the moment. I gave this book 5 stars since I wanted a high quality ML and python book which leads me through the models in a step-by-step way no matter how hard it is mathematically or programmtechnically. And I got this.negative:The pdf version has color pictures which is nice especially for multiline charts ( like page 212) where the b&w book just visually flat and some chart elements cannot be identified.

Python Machine Learning, Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow , Second Edition

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