Mastering Machine Learning with Scikit-Learn (Python)

Description

Cover Copyright Credits About the Author About the Reviewer www.PacktPub.com Customer Feedback Table of Contents Preface Chapter 1: The Fundamentals of Machine Learning Defining machine learning Learning from experience Machine learning tasks Training data, testing data, and validation data Bias and variance An introduction to scikit-learn Installing scikit-learn Installing using pip Installing on Windows Installing on Ubuntu 16.04 Installing on Mac OS Installing Anaconda Verifying the installation Installing pandas, Pillow, NLTK, and matplotlib Summary Chapter 2: Simple Linear Regression Simple linear regression Evaluating the fitness of the model with a cost function Solving OLS for simple linear regression Evaluating the model Summary Chapter 3: Classification and Regression with k-Nearest Neighbors K-Nearest Neighbors Lazy learning and non-parametric models Classification with KNN Regression with KNN Scaling features Summary Chapter 4: Feature Extraction Extracting features from categorical variables Standardizing features Extracting features from text The bag-of-words model Stop word filtering Stemming and lemmatization Extending bag-of-words with tf-idf weights Space-efficient feature vectorizing with the hashing trick Word embeddings Extracting features from images Extracting features from pixel intensities Using convolutional neural network activations as features Summary Chapter 5: From Simple Linear Regression to Multiple Linear Regression Multiple linear regression Polynomial regression Regularization Applying linear regression Exploring the data Fitting and evaluating the model Gradient descent Summary Chapter 6: From Linear Regression to Logistic Regression Binary classification with logistic regression Spam filtering Binary classification performance metrics Accuracy Precision and recall Calculating the F1 measure ROC AUC Tuning models with grid search Multi-class classification Multi-class classification performance metrics Multi-label classification and problem transformation Multi-label classification performance metrics Summary Chapter 7: Naive Bayes Bayes' theorem Generative and discriminative models Naive Bayes Assumptions of Naive Bayes Naive Bayes with scikit-learn Summary Chapter 8: Nonlinear Classification and Regression with Decision Trees Decision trees Training decision trees Selecting the questions Information gain Gini impurity Decision trees with scikit-learn Advantages and disadvantages of decision trees Summary Chapter 9: From Decision Trees to Random Forests and Other Ensemble Methods Bagging Boosting Stacking Summary Chapter 10: The Perceptron The perceptron Activation functions The perceptron learning algorithm Binary classification with the perceptron Document classification with the perceptron Limitations of the perceptron Summary Chapter 11: From the Perceptron to Support Vector Machines Kernels and the kernel trick Maximum margin classification and support vectors Classifying characters in scikit-learn Classifying handwritten digits Classifying characters in natural images Summary Chapter 12: From the Perceptron to Artificial Neural Networks Nonlinear decision boundaries Feed-forward and feedback ANNs Multi-layer perceptrons Training multi-layer perceptrons Backpropagation Training a multi-layer perceptron to approximate XOR Training a multi-layer perceptron to classify handwritten digits Summary Chapter 13: K-means Clustering K-means Local optima Selecting K with the elbow method Evaluating clusters Image quantization Clustering to learn features Summary Chapter 14: Dimensionality Reduction with Principal Component Analysis Principal component analysis Variance, covariance, and covariance matrices Eigenvectors and eigenvalues Performing PCA Visualizing high-dimensional data with PCA Face recognition with PCA Summary Index