Thoughtful Machine Learning with Python

【实例简介】
Author: Matthew Kirk ISBN-10: 1491924136 Year: 2017 Pages: 216 Language: English File size: 8.6 MB File format:PDF Category: Data Mining Book Description: Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to inte
Thoughtful Machine Learning with Python A Test-Driven approach Matthew Kirk Beijing. Boston. Farnham. Sebastopol. Tokyo OREILLY Thoughtful Machine Learning with Python by Matthew Kirk Copyright O 2017 Matthew Kirk. All rights reserved Printed in the united states of america Published by o reilly media, InC, 1005 Gravenstein Highway North, Sebastopol, CA95472 OReilly books may be purchased for educational, business, or sales promotional use Online editions are alsoavailableformosttitles(http://oreilly.com/safari).Formoreinformationcontactourcorporate/insti tutionalsalesdepartment:800-998-9938orcorporate@oreilly.com Editors: Mike Loukides and Shannon Cutt Indexer: Wendy catalano Production editor: Nicholas adams Interior Designer: David Futato Copyeditor: James Fraleigh over Designer: Randy Comer Proofreader: Charles roumeliotis Illustrator rebecca demarest January 2017: First edition Revision History for the First Edition 2017-01-10 First Release Seehttp://oreilly.com/catalog/errata.csp?isbn=9781491924136forreleasedetails The O Reilly logo is a registered trademark of O Reilly Media, Inc. Thoughtful Machine learning with Python, the cover image, and related trade dress are trademarks of O Reilly media, Inc While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights 978-1-491-92413-6 Table of contents Preface X 1. Probably Approximately Correct Software.................,.. Writing Software Right SOLID Testing or TDD Refactoring Writing the right Software Writing the Right Software with Machine Learning 2245677 What Exactly Is Machine Learning The High Interest Credit Card Debt of Machine Learning 8 SOLID Applied to Machine Learning Machine Learning Code Is Complex but Not Impossible 12 TDD Scientific Method 2.0 12 Refactoring Our Way to Knowledge The Plan for the book 13 2. AQuick Introduction to Machine Learning...................15 What Is Machine Learning? 15 upervised Learning 15 Unsupervised Learning Reinforcement Learning 17 What Can Machine Learning Accomplish? 17 Mathematical Notation Used Throughout the book 18 Conclusion 3. K-Nearest Neighbors...... How Do You determine Whether You Want to buy a Houser?·………∴∴…21 21 How Valuable is that house 22 Hedonic Regression 22 What Is a Neighborhood? 23 K-Nearest Neighbors 24 Mr Ks Nearest Neighborhood 25 Distances 25 Triangle Inequality 25 Geometrical Distance 26 Computational Distance 27 Statistical distances 29 Curse of dimensionality 31 How Do We pick K? 32 Guessing K 32 Heuristics for Picking K 33 Valuing Houses in Seattle 35 about the data 36 General Strategy 36 Coding and Testing design 36 KNN Regressor Construction 37 KNN Testin 39 onclusion 41 4. Naive Bayesian Classification.......... 43 Using bayes Theorem to Find Fraudulent Orders 43 Conditional Probabilities 44 Probability symbols 44 Inverse Conditional Probability(aka Bayes Theorem) 46 Naive bayesian Classifier The Chain rule Naivete in Bayesian Reasoning 47 Pseudocount Spam Filter 50 Setup Notes 5 Coding and Testing Design 50 Data Source 51 Email class 51 Tokenization and Context 54 Spamtrainer 56 Error Minimization Through Cross-Validation 62 Conclusion 65 5. Decision trees and random forests The Nuances of mushrooms 68 Classifying mushrooms Using a Folk Theorem 69 iv Table of Contents Finding an Optimal Switch Point 70 Information gain 71 GINI Impurity 72 Variance reduction Pruning trees Ensemble learning 74 Writing a Mushroom Classifier Conclusion 83 6. Hidden markov models...4.4.4...85 Tracking User Behavior Using State Machines 85 Emissions/Observations of Underlying states 87 Simplification Through the Markov Assumption 89 USing Markov Chains Instead of a Finite State Machine 89 Hidden markov model Evaluation: Forward-Backward Algorithm Mathematical Representation of the Forward-Backward algorithm Using User Behavior 91 The Decoding problem Through the viterbi algorithm 94 The Learning problem 5 Part-of-Speech Tagging with the Brown Corpus 95 Setup notes 96 Coding and Testing Design 96 The Seam of Our Part-of-Speech Tagger: CorpusParser Writing the Part-of-Speech Tagger 9 Cross-Validating to Get Confidence in the Model 105 How to make This model better 106 Conclusion 106 7. Support vector Machines. Customer happiness as a Function of what They say 108 Sentiment Classification Using Svms 108 The Theory behind svms 109 Decision boundary 110 Maximizing boundaries 111 Kernel trick: Feature transformation 111 Optimizing with Slack 114 Sentiment analyzer 114 114 Coding and Testing DesigN 115 SVM Testing strategies 116 Corpus Class Table of conten CorpusSet Class 119 Model validation and the sentiment Classifier 122 Aggregating Sentiment 125 Exponentially weighted Moving average 126 Mapping Sentiment to Bottom Line 127 C onclusion 128 8. Neural Networks 129 What Is a Neural Network? 130 History of Neural Nets 130 Boolean logic 130 Percepti 131 How to Construct Feed-Forward Neural Nets 131 Input layer p 132 Hidden lavers 134 Neurons 135 Activation functions 136 Output layer 141 Training Algorithms 141 The Delta rule 142 Back propagation 142 Quick 143 RProp 143 Building Neural Networks 145 How many hidden layers? 145 How Many Neurons for Each layer 146 Tolerance for Error and Max epochs 146 USing a Neural Network to Classify a Language 147 Setup notes 147 Coding and Testing Design 147 The data 148 Writing the Seam Test for Language 148 Cross-Validating Our Way to a Network Class 151 Tuning the Neural Network 154 Precision and recall for Neural Networks 154 Wrap-Up of Example 154 Conclusion 155 9.〔 lustering 157 Studying Data Without Any Bias 157 User Cohorts 158 Testing Cluster Mappings 160 Table of contents Fitness of a Cluster 160 Silhouette coefficient 160 Comparing results to ground Truth 161 K-Means Clustering 161 The K-Means algorithm 161 Downside of K-Means Clustering 163 EM Clustering 163 algorithm 164 The Impossibility Theorem Example: Categorizing music 166 tup n 166 Gathering the Data 166 Coding design 167 Analyzing the Data with K-Means 168 EM Clustering Our Data 169 The Results from the EM Jazz Clustering 174 Conclusion 176 10. Improving Models and Data Extraction Debate club 177 Picking better Data 178 Feature selection 178 Exhaustive search 180 Random feature selection 182 a Better Feature Selection algorithm 182 Minimum Redundancy maximum Relevance Feature Selection 183 Feature Transformation and Matrix Factorization 185 Principal component analysis 185 Independent component analy 186 Ensemble learning 188 Baggio 189 Boosting 189 Conclusion 191 11. Putting It Together: Conclusion 93 Machine Learning Algorithms Revisited How to use this information to solve problems 195 What's Next for You? 195 ndeX。, 197 Table of contents|ⅶi 【实例截图】
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