Mastering Machine Learning with scikit-learn 第二版 2017

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 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 Bayes' theorem 115 Generative and discriminative models 117 [ iii ] Naive Bayes 118 Assumptions of Naive Bayes 119 Naive Bayes with scikit-learn 120 Summary 124 Chapter 8: Nonlinear Classification and Regression with Decision Trees 125 Decision trees 125 Training decision trees 127 Selecting the questions 128 Information gain 131 Gini impurity 136 Decision trees with scikit-learn 137 Advantages and disadvantages of decision trees 139 Summary 140 Chapter 9: From Decision Trees to Random Forests and Other Ensemble Methods 141 Bagging 141 Boosting 144 Stacking 146 Summary 148 Chapter 10: The Perceptron 149 The perceptron 149 Activation functions 150 The perceptron learning algorithm 152 Binary classification with the perceptron 153 Document classification with the perceptron 161 Limitations of the perceptron 162 Summary 163 Chapter 11: From the Perceptron to Support Vector Machines 164 Kernels and the kernel trick 165 Maximum margin classification and support vectors 169 Classifying characters in scikit-learn 172 Classifying handwritten digits 172 Classifying characters in natural images 175 Summary 177 Chapter 12: From the Perceptron to Artificial Neural Networks 178 Nonlinear decision boundaries 179 [ iv ] Feed-forward and feedback ANNs 180 Multi-layer perceptrons 181 Training multi-layer perceptrons 183 Backpropagation 184 Training a multi-layer perceptron to approximate XOR 189 Training a multi-layer perceptron to classify handwritten digits 192 Summary 193 Chapter 13: K-means 194 Clustering 194 K-means 197 Local optima 203 Selecting K with the elbow method 204 Evaluating clusters 207 Image quantization 209 Clustering to learn features 211 Summary 214 Chapter 14: Dimensionality Reduction with Principal Component Analysis 215 Principal component analysis 215 Variance, covariance, and covariance matrices 220 Eigenvectors and eigenvalues 222 Performing PCA 224 Visualizing high-dimensional data with PCA 227 Face recognition with PCA 228 Summary 231 Index 233