Mastering Machine Learning with scikit-learn 第二版 2017

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Mastering Machine Learning with scikit-learn (2 ed) (True PDF + AWZ3 + codes) Table of Contents Preface 1 Chapter 1: The Fundamentals of Machine Learning 6 Defining machine learning 6 Learning from experience 8 Machine learning tasks 9 Training data, testing data, and validation data 10 Bias and variance 13 An introduction to scikit-learn 15 Installing scikit-learn 16 Installing using pip 17 Installing on Windows 17 Installing on Ubuntu 16.04 17 Installing on Mac OS 17 Installing Anaconda 18 Verifying the installation 18 Installing pandas, Pillow, NLTK, and matplotlib 18 Summary 19 Chapter 2: Simple Linear Regression 20 Simple linear regression 20 Evaluating the fitness of the model with a cost function 25 Solving OLS for simple linear regression 27 Evaluating the model 29 Summary 31 Chapter 3: Classification and Regression with k-Nearest Neighbors 32 K-Nearest Neighbors 32 Lazy learning and non-parametric models 33 Classification with KNN 34 Regression with KNN 42 Scaling features 44 Summary 47 Chapter 4: Feature Extraction 48 Extracting features from categorical variables 48 Standardizing features 49 [ ii ] Extracting features from text 50 The bag-of-words model 50 Stop word filtering 53 Stemming and lemmatization 54 Extending bag-of-words with tf-idf weights 57 Space-efficient feature vectorizing with the hashing trick 59 Word embeddings 61 Extracting features from images 64 Extracting features from pixel intensities 65 Using convolutional neural network activations as features 66 Summary 68 Chapter 5: From Simple Linear Regression to Multiple Linear Regression 70 Multiple linear regression 70 Polynomial regression 74 Regularization 79 Applying linear regression 80 Exploring the data 81 Fitting and evaluating the model 84 Gradient descent 86 Summary 90 Chapter 6: From Linear Regression to Logistic Regression 91 Binary classification with logistic regression 92 Spam filtering 94 Binary classification performance metrics 95 Accuracy 97 Precision and recall 98 Cal
Mastering Machine Learning with scikit-learn Second edition Copyright o 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 author 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: October 2014 Second published July 2017 Production reference: 1200717 Published by packt Publishing ltd Livery Place 35 Livery Street Birmingham B3 2PB. UK ISBN978-1-78829-987-9 www.pAcktpub.com Credits Author Copy Editors Gavin hackling Safis Editing Vikrant phadka Reviewer Project Coordinator Oleg okun Nidhi Joshi Commissioning editor Proofreader Amey varangaonkar Safis Editing Acquisition Editor Indexer Aman Singh Tejal Daruwale soni Content Development Editor Graphics Aishwarya Pandere Tania dutta Technical editor Production Coordinator Suwarna patil Arvindkumar Gupta About the author Gavin hackeling is a data scientist and author he was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition and semantic segmentation. An alumnus of the university of north carolina and New York University, he lives in Brooklyn with his wife and cat I would like to thank my wife Hallie, and the scikti-learn community about the reviewer Oleg Okun is a machine learning expert and an author/editor of four books, numerous journal articles, and conference papers. His career spans more than a quarter of a century He was employed in both academia and industry in his motherland, Belarus and abroad (Finland, Sweden, and Germany). His work experience includes document image analysis, fingerprint biometrics, bioinformatics, online/offline marketing analytics, credit scoring analytics and text analytics He is interested in all aspects of distributed machine learning and the Internet of Things Oleg currently lives and works in Hamburg, Germany I would like to express my deepest gratitude to my parents for everything that they have done for me www.paCktpub.com Forsupportfilesanddownloadsrelatedtoyourbookpleasevisitwww.packtpub.com Did you know that packt offers e book versions of every book published, with pdf and epubfilesavailableyoUcanupgradetotheebookversionatwww.packtpub.comandasa print book customer, you are entitled to a discount on the ebook copy. Get in touch with us atservice@packtpub.comformoredetails. 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To help us improve, please leave us an honest review on this book's amazon page athttps://www.amazoncom/dp/1788299876 If you'd like to join our team of regular reviewers, you can e-mail us at ustomerreviewsapacktpub com We award our regular reviewers with free eBooks and videos in exchange for their valuable feedback. Help us be relentless in improving our products Table of contents Preface Chapter 1: The Fundamentals of Machine Learning Defining machine learning Learning from experience 6689 Machine learning tasks Training data, testing data, and validation data 10 Bias and variance An introduction to scikit-earn 15 Installing scikit-learn 16 Installing using pip Installing on Windows Installing on Ubuntu 16.04 Installing on Mac Os Installing Anaconda 18 Verifying the installation 18 Installing pandas, Pillow, NLTK, and matplotlib 18 Summary Chapter 2: Simple linear regression 20 simple linear regression 20 Evaluating the fitness of the model with a cost function 25 Solving oLs for simple linear regression Evaluating the model 29 Summary 31 Chapter 3: Classification and Regression with k-Nearest Neighbors 32 K-Nearest Neighbors 32 Lazy learning and non-parametric models 33 Classification with KNN 34 Regression with KNN 42 Scaling features 44 Summary 47 Chapter 4: Feature Extraction 48 Extracting features from categorical variables 48 Standardizing features 49 Extracting features from text 50 The bag-of-words model 50 Stop word filtering Stemming and lemmatization 54 Extending bag-of-words with tf-idf weights 57 Space-efficient feature vectorizing with the hashing trick 59 Word embeddings Extracting features from images 64 Extracting features from pixel intensities 65 Using convolutional neural network activations as features 66 Summary 68 Chapter 5: From Simple Linear Regression to Multiple Linear Regression 70 Multiple linear regression 70 Polynomial regression 74 Regularization 79 Applying linear regression 80 Exploring the data Fitting and evaluating the model 84 Gradient descent 86 Summary 0 Chapter 6: From Linear Regression to Logistic Regression 91 Binary classification with logistic regression Spam filtering 94 Binary classification performance metrics 95 Accuracy 97 Precision and recall 98 Calculating the F1 measure 99 ROC AUC 100 Tuning models with grid search 102 Multi-class classification 104 Multi-class classification performance metrics 107 Multi-label classification and problem transformation 108 Multi-label classification performance metrics 113 Summary 114 Chapter 7: Naive Bayes 115 Bayestheorem 115 Generative and discriminative models 117 [i]

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