#### Mastering Machine Learning with scikit-learn - Second Edition

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#### Mastering Machine Learning with scikit-learn - Second Edition

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#### Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book 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. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Table of Contents (22 chapters)

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

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

Index

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