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详细说明：Building-Machine-Learning-Systems-with-Python.pdfBuilding Machine Learning Systems with Python Second edition 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 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: July 2013 Second edition march 2015 Production reference: 1230315 Published by Packt Publishing Ltd Livery place 35 Livery street Birmingham B3 2PB,UK ISBN978-1-78439-277-2 www.packtpub.com Credits Authors Project Coordinator Luis pedro coelho Nikhil nair Willi Richert Proofreaders R Simran bhogal Matthieu bucher Lawrence a. herman Maurice HT Ling Linda morris Radim rehurek Paul hindle Commissioning editor Indexer Kartikey Pandey Hemangini bari Acquisition Editors Graphics Greg Wild Sheetal Aute Richard hai Abhinash sahu Kartikey pande Production coordinator Content Development Editor Arvindkumar Gupta Arun nadar Cover Work Technical editor Arvind kumar gupta Pankaj Kadal Copy Editors Relin he Sameen Siddiqui Laxmi subramanian About the authors Luis Pedro Coelho is a computational biologist: someone who uses computers as a tool to understand biological systems. In particular, Luis analyzes dna from microbial communities to characterize their behavior luis has also worked extensively in bioimage informatics-the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets Luis has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications Luis started developing open source software in 1998 as a way to apply real code to what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on the popular computer vision package for python and mahotas, as well as the contributor of several machine earner ng Luis currently divides his time between Luxembourg and Heidelberg I thank my wife, Rita, for all her love and support and my daughter, Anna, for being the best thing ever Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden markov models and bayesian networks to let heterogeneous robots learn by imitation currently, he works for microsoft in the Core relevance team of bing where he is involved in a variety of ml areas such as active learning, statistical machine translation, and growing decision trees This book would not have been possible without the support of my wife, Natalie, and my sons, Linus and Moritz. I am especially grateful for the many fruitful discussions with my current or previous managers, Andreas Bode, Clemens Marschner, Hongyan Zhou, and Eric Crestan, as well as my colleagues and friends Tomasz Marciniak, Cristian Eigel, Oliver Nichocrster, and Philipp Adelt. The interesting ideas are most likely from them; the bugs belong to me About the reviewers Matthieu brucher holds an engineering degree from the Ecole Superieure manifold learning from the Universite de Stras bourg, France. He currently holds sed d Electricite(Information, Signals, Measures), France and has a PhD in unsuper an IIpc software developer position in an oil company and is working on the next generation reservoir simulation Maurice ht ling has been programming in Python since 2003. Having completed his PhD in Bioinformatics and BSc(Hons. in Molecular and Cell Biology from The University of Melbourne, he is currently a research fellow at Nanyang Technological University, Singapore, and an Honorary Fellow at The University of Melbourne, Australia. Maurice is the Chief editor for Computational and mathematical biology, and co-editor for The Python Papers. Recently, Maurice cofounded the first synthetic biology start-up in Singapore advanceSyn pte. Ltd. as the director and chief technology Officer. I lis research interests lies in life-biological life artificial life and artificial intelligence-using computer science and statistics as tools to understand life and its numerous aspects. In his free time maurice likes to read, enjoy a cup of coffee write his personal journal, or philosophize on various aspects of life. His website and Linkedinprofilearehttp://maurice.vodien.comandhttp://www.linkedin.com/ in/mauriceling, respectively. Radim rehurek is a tech geek and developer at heart He founded and led the research department at Seznam. cz, a major search engine company in central europe After finishing his PhD, he decided to move on and spread the machine learning love, starting his own privately owned r&d company, RaRe consulting ltd. Rare specializes in made-to-measure data mining solutions, delivering cutting-edge systems for clients ranging from large multinationals to nascent start-ups Radim is also the author of a number of popular open source projects, including gensim and smart_open A big fan of experiencing different cultures, Radim has lived around the globe with his wife for the past decade, with his next steps leading to South Korea. No matter where he stays, Radim and his team always try to evangelize data-driven solutions and help companies worldwide make the most of their machine learning opportunities Www.Packtpub.com Support files, eBooks, discount offers, and more Forsupportfilesanddownloadsrelatedtoyourbookpleasevisitwww.packtpui.cOm Did you know that Packt offers e Book versions of every book published, with PDF ndepuBfilesavailableYoucanupgradetotheeBookversionatwww.packtpub.Com and as a print book customer, you are entitled to a discount on the e Book copy. Get in touch with us at servicepacktpub com for more details Atwww.Packtpub.comyoucanalsoreadacollectionoffreetechnicalarticle g up for a range of free newsletters and receive exclusive discounts and offers on Packt books and ebooks JPACKTLIB https://www2.packtpub.ccm/books/subscription/packtlib Do you need instant solutions to your It questions? PacktLib is Packt's online digital book library. Here, you can search, access, and read Packt's entire library of books Why subscribe? Fully searchable across every book published by packt Copy and paste, print, and bookmark content On demand and accessible via a web browser Free access for packt account holders AouhaveanaccountwithPacktatwww.packtpub.comyoucanusethistoaccess If PacktLib today and view g entirely free books Simply use your login credentials for immediate access Table of contents Preface Chapter 1: Getting Started with Python Machine Learning Machine learning and python -a dream team What the book will teach you(and what it will not) What to do when you are stuck Getting started Introduction to NumPy, SciPy, and matplotlib Installing Python Chewing data efficiently with NumPy and intelligently with SciPy Learning NumPy 123456667901 Indexing Handling nonexisting values Comparing the runtime Learning SciPy 12 Our first(tiny)application of machine learning Reading in the data 14 Preprocessing and cleaning the data 15 Choosing the right model and learning algorithm Before building our first model 18 Starting with a simple straight line 18 Towards some advanced stuff 20 Stepping back to go forward -another look at our data Training and testing Answering our initial question Summary 28 Chapter 2: Classifying with Real-world Examples 29 The iris dataset 30 Visualization is a good first step 30 Building our first classification model 32 Evaluation-holding out data and cross-validation

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