Raschka Sebastian - Python Machine Learning - Second Edition
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Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python.
This Second Edition of Sebastian Raschkas Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning.
Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning.
Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, youll be ready to meet the new data analysis opportunities in todays world .
Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning.
What you will learn Use the key frameworks of data science, machine learning, and deep learning Ask new questions of your data through machine learning models and neural networks Work with the most powerful Python open-source libraries in machine learning Build deep learning applications using Keras and TensorFlow Embed your machine learning model in accessible web applications Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Analyze images using deep learning techniques Use sentiment analysis to delve deeper into textual and social media dataRaschka Sebastian: author's other books
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Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: September 2015
Second edition: September 2017
Revision log for the second edition:
First release: 14-09-2017
Second release: 15-09-2017
Third release: 23-10-2017
Fourth release: 04-09-2018
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78712-593-3
www.packtpub.com
Authors
Sebastian Raschka
Vahid Mirjalili
Reviewers
Jared Huffman
Huai-En, Sun (Ryan Sun)
Acquisition Editor
Frank Pohlmann
Content Development Editor
Chris Nelson
Project Editor
Monika Sangwan
Technical Editors
Bhagyashree Rai
Nidhisha Shetty
Copy Editor
Safis Editing
Project Coordinator
Suzanne Coutinho
Proofreader
Safis Editing
Indexer
Tejal Daruwale Soni
Graphics
Kirk D'Penha
Production Coordinator
Arvindkumar Gupta
Sebastian Raschka , the author of the bestselling book, Python Machine Learning , has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning including a machine learning tutorial at SciPythe leading conference for scientific computing in Python.
While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.
His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017 as well as the ACM Computing Reviews Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.
I would like to take this opportunity to thank the great Python community and developers of open source packages who helped me create the perfect environment for scientific research and data science. Also, I want to thank my parents who always encouraged and supported me in pursuing the path and career that I was so passionate about.
Special thanks to the core developers of scikit-learn. As a contributor to this project, I had the pleasure to work with great people who are not only very knowledgeable when it comes to machine learning but are also excellent programmers.
Lastly, I'd like to thank Elie Kawerk, who volunteered to review the book and provided valuable feedback on the new chapters.
Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the department of computer science and engineering at Michigan State University.
Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.
While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.
I would like to thank my PhD advisor, Dr. Arun Ross, for giving me the opportunity to work on novel problems in his research lab. I also like to thank Dr. Vishnu Boddeti for inspiring my interests in deep learning and demystifying its core concepts.
Jared Huffman is an entrepreneur, gamer, storyteller, machine learning fanatic, and database aficionado. He has dedicated the past 10 years to developing software and analyzing data. His previous work has spanned a variety of topics, including network security, financial systems, and business intelligence, as well as web services, developer tools, and business strategy. Most recently, he was the founder of the data science team at Minecraft, with a focus on big data and machine learning. When not working, you can typically find him gaming or enjoying the beautiful Pacific Northwest with friends and family.
I'd like to thank Packt for giving me the opportunity to work on such a great book, my wife for the constant encouragement, and my daughter for sleeping through most of the late nights while I was reviewing and debugging code.
Huai-En, Sun (Ryan Sun) holds a master's degree in statistics from the National Chiao Tung University. He is currently working as a data scientist for analyzing the production line at PEGATRON. Machine learning and deep learning are his main areas of research.
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