Machine Learning with scikit-learn Quick Start Guide

Preface

The fundamental aim of this book is help its readers quickly deploy, optimize, and evaluate every kind of machine learning algorithm that scikit-learn provides in an agile manner.

Readers will learn how to deploy supervised machine learning algorithms, such as logistic regression, k-nearest neighbors, linear regression, Support Vector Machines, Naive Bayes, and tree-based algorithms, in order to solve classification and regression machine learning problems.

Readers will also learn how to deploy unsupervised machine learning algorithms such as the k-means algorithm in order to cluster unlabeled data into groups.

Finally, readers will be provided with different techniques to visually interpret and evaluate the performance of the algorithms ...