Book Description
Apply effective learning algorithms to realworld problems using scikitlearn
In Detail
This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervisedunsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.
You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.
By the end of the book, you will be an expert in scikitlearn and will be well versed in machine learning
What You Will Learn
 Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics
 Predict the values of continuous variables using linear regression
 Create representations of documents and images that can be used in machine learning models
 Categorize documents and text messages using logistic regression and support vector machines
 Classify images by their subjects
 Discover hidden structures in data using clustering and visualize complex data using decomposition
 Evaluate the performance of machine learning systems in common tasks
 Diagnose and redress problems with models due to bias and variance
Publisher Resources
Table of Contents

Mastering Machine Learning with scikitlearn
 Table of Contents
 Mastering Machine Learning with scikitlearn
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. The Fundamentals of Machine Learning
 2. Linear Regression
 3. Feature Extraction and Preprocessing
 4. From Linear Regression to Logistic Regression
 5. Nonlinear Classification and Regression with Decision Trees
 6. Clustering with KMeans
 7. Dimensionality Reduction with PCA
 8. The Perceptron
 9. From the Perceptron to Support Vector Machines
 10. From the Perceptron to Artificial Neural Networks
 Index
Product Information
 Title: Mastering Machine Learning with scikitlearn
 Author(s):
 Release date: October 2014
 Publisher(s): Packt Publishing
 ISBN: 9781783988365