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英文原版

Table of Contents
Preface vii
Chapter 1: Giving Computers the Ability to Learn from Data 1
Building intelligent machines to transform data into knowledge 2
The three different types of machine learning 2
Making predictions about the future with supervised learning 3
Classification for predicting class labels 3
Regression for predicting continuous outcomes 4
Solving interactive problems with reinforcement learning 6
Discovering hidden structures with unsupervised learning 6
Finding subgroups with clustering 7
Dimensionality reduction for data compression 7
An introduction to the basic terminology and notations 8
A roadmap for building machine learning systems 10
Preprocessing – getting data into shape 11
Training and selecting a predictive model 12
Evaluating models and predicting unseen data instances 13
Using Python for machine learning 13
Installing Python packages 13
Summary 15
Chapter 2: Training Machine Learning Algorithms
for Classification 17
Artificial neurons – a brief glimpse into the early history
of machine learning 18
Implementing a perceptron learning algorithm in Python 24
Training a perceptron model on the Iris dataset 27
Adaptive linear neurons and the convergence of learning 33
Minimizing cost functions with gradient descent 34
Table of Contents
[ ii ]
Implementing an Adaptive Linear Neuron in Python 36
Large scale machine learning and stochastic gradient descent 42
Summary 47
Chapter 3: A Tour of Machine Learning Classifiers Using
Scikit-learn 49
Choosing a classification algorithm 49
First steps with scikit-learn 50
Training a perceptron via scikit-learn 50
Modeling class probabilities via logistic regression 56
Logistic regression intuition and conditional probabilities 56
Learning the weights of the logistic cost function 59
Training a logistic regression model with scikit-learn 62
Tackling overfitting via regularization 65
Maximum margin classification with support vector machines 69
Maximum margin intuition 70
Dealing with the nonlinearly separable case using slack variables 71
Alternative implementations in scikit-learn 74
Solving nonlinear problems using a kernel SVM 75
Using the kernel trick to find separating hyperplanes in higher
dimensional space 77
Decision tree learning 80
Maximizing information gain – getting the most bang for the buck 82
Building a decision tree 88
Combining weak to strong learners via random forests 90
K-nearest neighbors – a lazy learning algorithm 92
Summary 96
Chapter 4: Building Good Training Sets – Data Preprocessing 99
Dealing with missing data 99
Eliminating samples or features with missing values 101
Imputing missing values 102
Understanding the scikit-learn estimator API 102
Handling categorical data 104
Mapping ordinal features 104
Encoding class labels 105
Performing one-hot encoding on nominal features 106
Partitioning a dataset in training and test sets 108
Bringing features onto the same scale 110
Selecting meaningful features 112
Sparse solutions with L1 regularization 112
Table of Contents
[ iii ]
Sequential feature selection algorithms 118
Assessing feature importance with random forests 124
Summary 126
Chapter 5: Compressing Data via Dimensionality Reduction 127
Unsupervised dimensionality reduction via principal
component analysis 128
Total and explained variance 130
Feature transformation 133
Principal component analysis in scikit-learn 135
Supervised data compression via linear discriminant analysis 138
Computing the scatter matrices 140
Selecting linear discriminants for the new feature subspace 143
Projecting samples onto the new feature space 145
LDA via scikit-learn 146
Using kernel principal component analysis for nonlinear mappings 148
Kernel functions and the kernel trick 148
Implementing a kernel principal component analysis in Python 154
Example 1 – separating half-moon shapes 155
Example 2 – separating concentric circles 159
Projecting new data points 162
Kernel principal component analysis in scikit-learn 166
Summary 167
Chapter 6: Learning Best Practices for Model Evaluation
and Hyperparameter Tuning 169
Streamlining workflows with pipelines 169
Loading the Breast Cancer Wisconsin dataset 170
Combining transformers and estimators in a pipeline 171
Using k-fold cross-validation to assess model performance 173
The holdout method 173
K-fold cross-validation 175
Debugging algorithms with learning and validation curves 179
Diagnosing bias and variance problems with learning curves 180
Addressing overfitting and underfitting with validation curves 183
Fine-tuning machine learning models via grid search 185
Tuning hyperparameters via grid search 186
Algorithm selection with nested cross-validation 187
Looking at different performance evaluation metrics 189
Reading a confusion matrix 190
Optimizing the precision and recall of a classification model 191
Table of Contents
[ iv ]
Plotting a receiver operating characteristic 193
The scoring metrics for multiclass classification 197
Summary 198
Chapter 7: Combining Different Models for Ensemble Learning 199
Learning with ensembles 199
Implementing a simple majority vote classifier 203
Combining different algorithms for classification with majority vote 210
Evaluating and tuning the ensemble classifier 213
Bagging – building an ensemble of classifiers from
bootstrap samples 219
Leveraging weak learners via adaptive boosting 224
Summary 232
Chapter 8: Applying Machine Learning to Sentiment Analysis 233
Obtaining the IMDb movie review dataset 233
Introducing the bag-of-words model 236
Transforming words into feature vectors 236
Assessing word relevancy via term frequency-inverse
document frequency 238
Cleaning text data 240
Processing documents into tokens 242
Training a logistic regression model for document classification 244
Working with bigger data – online algorithms and
out-of-core learning 246
Summary 250
Chapter 9: Embedding a Machine Learning Model into
a Web Application 251
Serializing fitted scikit-learn estimators 252
Setting up a SQLite database for data storage 255
Developing a web application with Flask 257
Our first Flask web application 258
Form validation and rendering 259
Turning the movie classifier into a web application 264
Deploying the web application to a public server 272
Updating the movie review classifier 274
Summary 276
Table of Contents
[ v ]
Chapter 10: Predicting Continuous Target Variables
with Regression Analysis 277
Introducing a simple linear regression model 278
Exploring the Housing Dataset 279
Visualizing the important characteristics of a dataset 280
Implementing an ordinary least squares linear regression model 285
Solving regression for regression parameters with gradient descent 285
Estimating the coefficient of a regression model via scikit-learn 289
Fitting a robust regression model using RANSAC 291
Evaluating the performance of linear regression models 294
Using regularized methods for regression 297
Turning a linear regression model into a curve – polynomial
regression 298
Modeling nonlinear relationships in the Housing Dataset 300
Dealing with nonlinear relationships using random forests 304
Decision tree regression 304
Random forest regression 306
Summary 309
Chapter 11: Working with Unlabeled Data – Clustering Analysis 311
Grouping objects by similarity using k-means 312
K-means   315
Hard versus soft clustering 317
Using the elbow method to find the optimal number of clusters 320
Quantifying the quality of clustering via silhouette plots 321
Organizing clusters as a hierarchical tree 326
Performing hierarchical clustering on a distance matrix 328
Attaching dendrograms to a heat map 332
Applying agglomerative clustering via scikit-learn 334
Locating regions of high density via DBSCAN 334
Summary 340
Chapter 12: Training Artificial Neural Networks for Image
Recognition 341
Modeling complex functions with artificial neural networks 342
Single-layer neural network recap 343
Introducing the multi-layer neural network architecture 345
Activating a neural network via forward propagation 347
Table of Contents
[ vi ]
Classifying handwritten digits 350
Obtaining the MNIST dataset 351
Implementing a multi-layer perceptron 356
Training an artificial neural network 365
Computing the logistic cost function 365
Training neural networks via backpropagation 368
Developing your intuition for backpropagation 372
Debugging neural networks with gradient checking 373
Convergence in neural networks 379
Other neural network architectures 381
Convolutional Neural Networks 381
Recurrent Neural Networks 383
A few last words about neural network implementation 384
Summary 385
Chapter 13: Parallelizing Neural Network Training with Theano 387
Building, compiling, and running expressions with Theano 388
What is Theano? 390
First steps with Theano 391
Configuring Theano 392
Working with array structures 394
Wrapping things up – a linear regression example 397
Choosing activation functions for feedforward neural networks 401
Logistic function recap 402
Estimating probabilities in multi-class classification via the
softmax function 404
Broadening the output spectrum by using a hyperbolic tangent 405
Training neural networks efficiently using Keras 408
Summary 414
Index 417