Machine Learning in Action

Description

“Machine Learning in Action” is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you will use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. As you work through the numerous examples, you will explore key topics like classification, numeric prediction, and clustering. Along the way, you will be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks. This book is written for hobbyists and developers. A background in Java is helpful-no prior experience with Android is assumed.

About the Author

Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter hold four US patents and his work has been published in two academic journals. He is actively involved with research on community detection and runs a software consulting business that focuses on scientific computing solutions.

Table of Contents

Part 1- Classification

· Machine learning basics

· Classifying with k-Nearest Neighbors

· Splitting datasets one feature at a time: decision trees

· Classifying with probability theory: naïve Bayes

· Logistic regression

· Support vector machines 101

· Improving classification with the AdaBoost meta-algorithm

Part 2- Forecasting Numeric Values With Regression

· Predicting numeric values: regression 153

· Tree-based regression

Part 3- Unsupervised Learning

· Grouping unlabeled items using k-means clustering

· Association analysis with the Apriori algorithm

· Efficiently finding frequent itemsets with FP-growth

Part 4- Additional Tools

· Using principal component analysis to simplify data

· Simplifying data with the singular value decomposition

· Big data and MapReduce