Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn......Page 5
 Table of Contents......Page 2
 Mastering Machine Learning with scikit-learn......Page 6
 Credits......Page 7
 About the Author......Page 9
 About the Reviewers......Page 10
   Free access for Packt account holders......Page 11
 Preface......Page 13
  What this book covers......Page 14
  What you need for this book......Page 16
  Who this book is for......Page 17
  Conventions......Page 18
  Reader feedback......Page 19
  Customer support......Page 20
  Downloading the example code......Page 21
  Errata......Page 22
  Piracy......Page 23
  Questions......Page 24
 1. The Fundamentals of Machine Learning......Page 25
  Learning from experience......Page 26
  Machine learning tasks......Page 28
  Training data and test data......Page 30
  Performance measures, bias, and variance......Page 33
  An introduction to scikit-learn......Page 36
   Installing scikit-learn on Windows......Page 37
   Verifying the installation......Page 38
  Installing pandas and matplotlib......Page 40
  Summary......Page 41
  Simple linear regression......Page 42
   Evaluating the fitness of a model with a cost function......Page 45
   Solving ordinary least squares for simple linear regression......Page 47
  Evaluating the model......Page 50
  Multiple linear regression......Page 53
  Polynomial regression......Page 57
  Regularization......Page 62
   Exploring the data......Page 63
   Fitting and evaluating the model......Page 66
  Fitting models with gradient descent......Page 69
  Summary......Page 72
  Extracting features from categorical variables......Page 73
   The bag-of-words representation......Page 75
   Stop-word filtering......Page 78
   Stemming and lemmatization......Page 79
   Extending bag-of-words with TF-IDF weights......Page 81
   Space-efficient feature vectorizing with the hashing trick......Page 83
   Extracting features from pixel intensities......Page 85
   Extracting points of interest as features......Page 87
   SIFT and SURF......Page 89
  Data standardization......Page 91
  Summary......Page 92
  Binary classification with logistic regression......Page 93
  Spam filtering......Page 96
  Binary classification performance metrics......Page 99
   Accuracy......Page 100
   Precision and recall......Page 101
  Calculating the F1 measure......Page 104
  ROC AUC......Page 105
  Tuning models with grid search......Page 107
  Multi-class classification......Page 110
   Multi-class classification performance metrics......Page 113
  Multi-label classification and problem transformation......Page 115
   Multi-label classification performance metrics......Page 119
  Summary......Page 121
  Decision trees......Page 122
  Training decision trees......Page 124
   Selecting the questions......Page 125
   Information gain......Page 128
   Gini impurity......Page 133
  Decision trees with scikit-learn......Page 135
   Tree ensembles......Page 137
   The advantages and disadvantages of decision trees......Page 138
  Summary......Page 140
 6. Clustering with K-Means......Page 141
  Clustering with the K-Means algorithm......Page 142
   Local optima......Page 150
   The elbow method......Page 151
  Evaluating clusters......Page 155
  Image quantization......Page 157
  Clustering to learn features......Page 159
  Summary......Page 162
  An overview of PCA......Page 163
   Variance, Covariance, and Covariance Matrices......Page 168
   Eigenvectors and eigenvalues......Page 169
   Dimensionality reduction with Principal Component Analysis......Page 172
  Using PCA to visualize high-dimensional data......Page 176
  Face recognition with PCA......Page 178
  Summary......Page 181
 8. The Perceptron......Page 182
  Activation functions......Page 183
   The perceptron learning algorithm......Page 184
  Binary classification with the perceptron......Page 186
   Document classification with the perceptron......Page 194
  Limitations of the perceptron......Page 197
  Summary......Page 199
  Kernels and the kernel trick......Page 200
  Maximum margin classification and support vectors......Page 205
   Classifying handwritten digits......Page 208
   Classifying characters in natural images......Page 211
  Summary......Page 214
  Nonlinear decision boundaries......Page 215
   Multilayer perceptrons......Page 218
   Forward propagation......Page 220
   Backpropagation......Page 226
  Approximating XOR with Multilayer perceptrons......Page 241
  Classifying handwritten digits......Page 243
  Summary......Page 244
 Index......Page 245