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Python Machine Learning Copyright o 2015 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 rt 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, cither express or implied. Neither the author nor Pacl 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 Production reference: 1160915 Published by Packt Publishing ltd Livery place 35 Livery street Birmingham b3 2PB UK ISBN978-1-78355-513-0 Credits Author Copy Editors Sebastian raschka Roshni Banerjee Stephan Copestake Reviewers Richard dutton Project Coordinator Dave julian Kinjal Bari Vahid mirialilf Hamidreza sattari Proofreader Dmytro Taranovsky Safis Editing Indexer Commissioning Editor Hemangini bari Akkram hussain Graphics Acquisition Editors Sheetal Aute Rebecca youe Abhinash sahu Meeta rajar Production Coordinator Content Development Editor Riddhi tuljapurkar Shantanu N. Agade Cover work Technical editors Shantanu n. agade Madhunikita sunil chindarkar Taabish khan Foreword We live in the midst of a data deluge. According to recent estimates, 2.5 quintillion (108 bytes of data are generated on a daily basis. This is so much data that over 90 percent of the information that we store nowadays was generated in the past decade alone. Unfortunately, most of this information cannot be used by humans. Either the data is beyond the means of standard analytical methods, or it is simply too vast for our limited minds to even comprehend Through Machine Learning, we enable computers to process learn from, and draw actionable insights out of the otherwise impenetrable walls of big data From the massive supercomputers that support Google's search engines to the smartphones that we carry in our pockets, we rely on Machine learning to power most of the world around us-often, without even knowing it more about Machine Learning. What is Machine Learning and how does it work? As modern pioneers in the brave new world of big data, it then behooves us to lear How can I use Machine Learning to take a glimpse into the unknown, power my business or just find out what the internet at large thinks about my favorite movie All of this and more will be covered in the following chapters authored by my good friend and colleague, sebastian Raschka When away from taming my otherwise irascible pet dog, Sebastian has tirelessly devoted his free time to the open source Machine Learning community. Over the past several years, Sebastian has developed dozens of popular tutorials that cover topics in Machine learning and data visualization in Python. He has also developed and contributed to several open source Python packages, several of which are now part of the core Python Machine Learning workflow Owing to his vast expertise in this field i am confident that sebastian's insights into the world of machine Learning in Python will be invaluable to users of all experience levels. I wholeheartedly recommend this book to anyone looking to gain a broader and more practical understanding of Machine Learning Dr Randal s. olson Artificial Intelligence and Machine Learning Researcher, University of Pennsylvania About the author Sebastian Raschka is a Phd student at Michigan State University who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on github by analytics Vidhya Ile has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions and if he is not in front of the computer, he enjoys playing sports I would like to thank my professors, Arun ross and pang- Ning tan and many others who inspired me and kindled my great interest in pattern classification, machine learning, and data mining 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 a special thanks goes 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 want to thank you all for showing an interest in this book, and i sincerely hope that i can pass on my enthusiasm to join the great Python and machine learning communities About the reviewers Richard dutton started programming the ZX Spectrum when he was 8 years old and his obsession carried him through a confusing array of technologies and roles in the fields of technolo gy and financ He has worked with microsoft, and as a director at barclays his current obsession is a mashup of Python, machine learning, and block chain If he's not in front of a computer, he can be found in the gym or at home with a glass of wine while he looks at his iphone. he calls this balance Dave julian is an it consultant and teacher with over 15 years of experience. He has worked as a technician, project manager, programmer, and web developer. His current projects include developing a crop analysis tool as part of integrated pest management strategies in greenhouses he has a strong interest in the intersection of biology and technology with a belief that smart machines can help solve the world's most important problems Vahid mirjalili received his Phd in mechanical engineering from Michigan State University, where he developed novel techniques for protein structure refinement using molecular dynamics simulations. Combining his knowledge from the fields of statistics, data mining, and physics he developed powerful data-driven approaches that helped him and his research group to win two recent worldwide competitions for protein structure prediction and refinement, CASP, in 2012 and 2014 While working on his doctorate degree, he decided to join the Computer Science and Engineering Department at Michigan State University to specialize in the field of machine learning. His current research projects involve the development of unsupervised machine learning algorithms for the mining of massive datasets. He is also a passionate Python programmer and shares his implementations of clustering algorithmsonhispersonalwebsiteat Hamidreza sattari is an it professional and has been involved in several areas of software engineering, from programming to architecture, as well as management He holds a master's degree in software engineering from Herriot-Watt University, UK, and a bachelor's degree in electrical engineering(electronics) from Tehran azad University, Iran. In recent years, his areas of interest have been big data and machine Learning. He coauthored the book Spring Web Services 2 Cookbook and he maintains hisblogat Dmytro Taranovsky is a software engineer with an interest and background in Python, Linux, and machine learning. Originally from Kiev, Ukraine, he moved to the United States in 1996. From an early age, he displayed a passion for science and knowledge, winning mathematics and physics competitions. In 1999, he was chosen to be a member of the u. s Physics team. In 2005, he graduated from the Massachusetts Institute of Technology, majoring in mathematics. Later, he worked as a software engineer on a text transformation system for computer-assisted medical transcriptions(eScription). Although he originally worked on Perl, he appreciated the power and clarity of Python, and he was able to scale the system to very large data sizes. Afterwards, he worked as a software engineer and analyst for an lgorithmic trading firm. He also made significant contributions to the foundation of mathematics, including creating and developing an extension to the language of set theory and its connection to large cardinal axioms developing a notion of constructive truth, and creating a system of ordinal notations and implementing them in Python. He also enjoys reading likes to go outdoors and tries to make the world a better place Support files, e Books, discount offers, and more Did you know that Packt offers e Book versions of every book published, with PDF andepubfilesavailableyoucanupgradetotheebookversionatwww.packtPubcom and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at serviceapacktpub com for more details Atwww.packtPub.comyoucanalsoreadacollectionoffreetechnicalarticlessign up for a range of free newsletters and receive exclusive discounts and offers on packt ooks and ebooks ] PACKTLIB° https://www2.packtpub.ccm/books/subscription/packtlib Do you need instant solutions to your If questions? PacktLib is Packt's online digital book library. Here, you can search, access, and read Packt's entire library of books Why subscribe? 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