Mastering Machine Learning with scikit-learn – Second Edition

Mastering Machine Learning with scikit-learn – Second Edition examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods.(Limited-time offer)

Table of Contents

• The Fundamentals of Machine Learning
• Simple Linear Regression
• Classification and Regression with k-Nearest Neighbors
• Feature Extraction
• From Simple Linear Regression to Multiple Linear Regression
• From Linear Regression to Logistic Regression
• Naive Bayes
• Nonlinear Classification and Regression with Decision Trees
• From Decision Trees to Random Forests and Other Ensemble Methods
• The Perceptron
• From the Perceptron to Support Vector Machines
• From the Perceptron to Artificial Neural Networks
• K-means
• Dimensionality Reduction with Principal Component Analysis

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