Machine Learning For Dummies.pdf

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Book Description: Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn’t be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you’re maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to ‘speak’ certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner’s guide so you are armed with all you need to know about machine learning!
Machine Learning dummies a Wiley brand by john Paul Mueller and luca massaron miles A Wiley brand Machine Learning for dummies PublishedbyJohnWiley&sons,Inc.,111RiverStreet,Hoboken,Nj07030-5774,www.wiley.com Copyright c 2016 by John Wiley Sons, Inc, Hoboken, New Jersey Media and software compilation copyright o 2016 by John Wiley Sons, Inc. All rights reserved Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley Sons, Inc, 111 River Street,HobokenNj07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissions. Trademarks: Wiley, For Dummies, the Dummies Man logo, Dummies. com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley Sons, Inc. and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc is not associated with any product or vendor mentioned in this book. LIMIT OF LIABILITY/DISCLAIMER OF WARRANTY: THE PUBLISHER AND THE AUTHOR MAKE NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE ACCURACY OR COMPLETENESS OF THE CONTENTS OF THIS WORK AND SPECIFICALLY DISCLAIM ALL WARRANTIES. INCLUDING WITHOUT LIMITATION WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE. NO WARRANTY MAY BE CREATED OR EXTENDED BY SALES OR PROMOTIONAL MATERIALS. THE ADVICE AND STRATEGIES CONTAINED HEREIN MAY NOT BE SUITABLE FOR EVERY SITUATION. THIS WORK IS SOLD WITH THE UNDERSTANDING THAT THE PUBLISHER IS NOT ENGAGED IN RENDERING LEGAL ACCOUNTING, OR OTHER PROFESSIONAL SERVICES. IF PROFESSIONAL ASSISTANCE IS REQUIRED, THE SERVICES OF A COMPETENT PROFESSIONAL PERSON SHOULD BE SOUGHT. NEITHER THE PUBLISHER NOR THE AUTHOR SHALL BE LIABLE FOR DAMAGES ARISING HEREFROM. THE FACT THAT AN ORGANIZATION OR WEBSITE IS REFERRED TO IN THIS WORK AS A CITATION AND /OR A POTENTIAL SOURCE OF FURTHER INFORMATION DOES NOT MEAN THAT THE AUTHOR OR THE PUBLISHER ENDORSES THE INFORMATION THE ORGANIZATION OR WEBSITE MAY PROVIDE OR RECOMMENDATIONS IT MAY MAKE FURTHER. READERS SHOULD BE AWARE THAT INTERNET WEBSITES LISTED IN THIS WORK MAY HAVE CHANGED OR DISAPPEARED BETWEEN WHEN THIS WORK WAS WRITTEN AND WHEN IT IS READ For general information on our other products and services, please contact our Customer Care Department within the U.S. at 877-762-2974, outside the U.S. at 317-572-3993, or fax 317-572-4002 For technical support, please visit Www.wIley.com/techsupport Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand If this book refers to media such as a CD or dvd that is not included in the version you purchased, you may download this material at http://booksupport.wiley.comFormoreinformationaboutWileyproductsvisitwww.wiley.com Library of Congress Control Number: 2016940023 ISBN:978-1-119-24551-3 ISBN978-1-119-24577-3(ebk); ISBN PDF978-1-119-24575-9(ebk) Manufactured in the united states of america 10987654321 Contents at a glance Introduction Part 1: Introducing How Machines Learn CHAPTER 1: Getting the real Story about Al CHAPTER 2: Learning in the Age of Big Data .23 CHAPTER 3: Having a glance at the Future 35 Part 2: Preparing Your Learning Tools .45 CHAPTER 4: Installing an R Distribution 47 CHAPTER 5: Coding in R Using STudio. 63 CHAPTER 6: Installing a Python Distribution 89 CHAPTER7: Coding in Python Using Anaconda ,,,109 CHAPTER 8: Exploring Other Machine Learning Tools ....137 Part 3: Getting Started with the math basics .,,,,.145 CHAPTER 9: Demystifying the math Behind Machine Learning ....147 CHAPTER 10: Descending the right Curve 167 CHAPTER 11: Validating Machine Learning 181 CHAPTER 12: Starting with Simple Learners ....199 Part 4: Learning from Smart and Big Data 217 CHAPTER 13: Preprocessing Data 219 CHAPTER 14: Leveraging Similarity................. 237 CHAPTER 15: Working with Linear Models the Easy Way 257 CHAPTER 16: Hitting Complexity with Neural Networks .279 CHAPTER 17: Going a Step beyond Using Support Vector Machines....... 297 CHAPTER 18: Resorting to Ensembles of learners 315 Part 5: Applying Learning to Real Problems 331 CHAPTER 19: Classifying Images 333 CHAPTER 20: Scoring Opinions and Sentiments ...349 CHAPTER 21: Recommending Products and movies 369 Part 6: the part of tens 383 CHAPTER 22: Ten Machine Learning Packages to Master .385 CHAPTER 23: Ten Ways to Improve Your Machine Learning Models 391 INDEX 399 Table of contents INTRODUCTION About this book Foolish Assumptions. 2 Icons used in this book Beyond the book Where to go from here 5 PART 1: NTRODUCING HOW MACHINES LEARN 7 CHAPTER 1: Getting the Real Story about Al Moving beyond the Hype 10 Dreaming of Electric Sheep 11 Understanding the history of Al and machine learning .12 Exploring what machine learning can do for Al 13 Considering the goals of machine learning 13 Defining machine learning limits based on hardware 14 Overcoming Al Fantasies 15 Discovering the of al and machine learning 16 Considering the true uses of Al and machine learning .16 Being useful; being mundane 18 Considering the Relationship between Al and machine Learning..19 Considering Al and Machine Learning Specifications.............. 20 Defining the Divide between Art and Engineering 20 CHAPTER 2: Learning in the age of big data 23 Defining Big Data 24 Considering the Sources of Big Data 25 Building a new data source............. .26 Using existing data sources 27 Specifying the role of Statistics in Machine Learning. Locating test data sources 28 .29 Understanding the role of algorithms.......... 30 Defining what algorithms do ,30 Considering the five main techniques ,30 Defining What Training Means 32 CHAPTER 3: Having a Glance at the Future ,35 Creating Useful Technologies for the Future 36 Considering the role of machine learning in robots ...36 Using machine learning in health care. .......................37 Creating smart systems for various needs 37 Table of contents Using machine learning in industrial settings 38 Understanding the role of updated processors and other hardware 39 Discovering the New Work Opportunities with Machine Learning....... 39 Working for a machine 40 Working with machines 41 Repairing machines ,,41 Creating new machine learning tasks 42 Devising new machine learning environments ...,42 Avoiding the Potential Pitfalls of Future Technologies 43 PART 2: PREPARING YOUR LEARNING TOOLS 45 CHAPTER 4: Installing anR Distribution 47 Choosing an r distribution with machine learning in mind.... 48 Installing on Windows 49 alling r on linux 56 alling R on Mac OsⅩ 57 Downloading the datasets and Example Code 59 Understanding the datasets used in this book 59 Detining the code repository .,,,,,,,,,,,,60 Coding inR Using STudio 63 Understanding the Basic Data Types 64 Working with Vectors 66 Organizing data Using Lists 66 Forking with Matrices 67 Creating a basic matrix 68 Changing the vector arrangemet 69 Accessing individual elements 69 Naming the rows and columns 70 Interacting with Multiple Dimensions Using Arrays 71 Creating a basic array Naming the rows and columns 72 Creating a Data Frame 74 Understanding 74 Creating a basic data frame 76 Interacting with data frames 77 Expanding a data frame 79 Performing Basic Statistical Tasks 80 iking decisi ...80 Working with loops 82 Machine Learning For Dummies Performing looped tasks without loops 84 Working with functio 85 Finding mean and median .85 Charting your data 87 CHAPTER 6 Installing a Python Distribution...........89 Choosing a Python Distribution with Machine Learning in Mind.....90 Getting Continuum Analytics Anaconda 91 Getting Enthought Canopy Express .92 Getting python 93 Getting WinPython 93 Installing python on linux ,,,,..93 Installing Python on Mac OS X .94 Installing python on windows 96 Downloading the Datasets and Example Code 99 Using jupiter Notebook 100 Defining the code repository ..101 Understanding the datasets used in this book ..106 CHAPTER 7: Coding in Python Using anaconda 109 Working with Numbers and Logic 110 Performing variable assignments 112 Doing arithmetic∴ ,,,,,,113 Comparing data using Boolean expressions .,,,,,,,115 Creating and Using Strings 117 ng with Date 118 Creating and Using Functions ..119 Creating reusable function 119 Calling functions .121 Working with global and local variables...........123 Using Conditional and Loop Statements .124 Making decisions using the if statement 124 Choosing between multiple options using nested decisions.. 125 Performing repetitive tasks using for 126 Using the while statement 127 Storing Data Using Sets, Lists, and Tuples 128 Creating sets .128 Performing operations on sets 128 Creating lists ,,.129 Creating and using tuples 131 Defining Useful Ite 132 Indexing Data Using Dictionaries........ 134 Storing Code in modules 134 Table of Contents vll CHAPTEr&: Exploring Other Machine Learning Tools. .........137 Meeting the Precursors SAS, Stata, and SPSS..........138 earning in Academia with Weka 140 Accessing Complex algorithms Easily Using LIBSVM 141 Running As Fast As Light with Vowpal Wabbit 142 Visualizing with Knime and rapidminer.…… 143 Dealing with Massive Data by Using Spark 44 PART 3: GETTING STARTED WITH THE MATH BASICS.145 CHAPTER 9: Demystifying the Math Behind Machine Learning ...147 Norking with Data ,,,,,,,148 Creating a matrix. 150 Understanding basic operations....... ..152 Performing matrix multiplication ...152 Glancing at advanced matrix operations .155 Using vectorization effectively ....155 Exploring the world of probabilities 音鲁面 158 Operating on probabilities 159 Conditioning chance by Bayes'theorem ..160 Describing the Use of Statistics 63 CHAPTER 10: Descending the right Curve 167 Interpreting Learning As optimization ,,,168 Supervised learning 168 Unsupervised learning ..169 Reinforcement 169 The learning process · ,,,,,,170 Exploring Cost Functions .,,,,,,,,,,,,173 Descending the Error curve. ....174 Updating by Mini-Batch and online 177 CHAPTER 11: Validating Machine Learning 181 Checking out-of-Sample Errors 182 Looking for generalization 83 Getting to know the limits of bias 184 Keeping model co ity in mind 186 Keeping Solutions Balanced 188 Depicting learning curves .,,,189 Training, Validating, and Testing 191 Resorting to Cross-validation 191 Looking for Alternatives in va .193 VIll Machine Learning For Dummies

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orchisan 非常棒,谢谢分享。
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