Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Case study


In this section, we will perform a case study with real-world machine learning datasets to illustrate some of the concepts from Bayesian networks.

We will use the UCI Adult dataset, also known as the Census Income dataset (http://archive.ics.uci.edu/ml/datasets/Census+Income). This dataset was extracted from the United States Census Bureau's 1994 census data. The donors of the data is Ronny Kohavi and Barry Becker, who were with Silicon Graphics at the time. The dataset consists of 48,842 instances with 14 attributes, with a mix of categorical and continuous types. The target class is binary.

Business problem

The problem consists of predicting the income of members of a population based on census data, specifically, whether their income is greater than $50,000.

Machine learning mapping

This is a problem of classification and this time around we will be training Bayesian graph networks to develop predictive models. We will be using linear, non-linear, and ensemble algorithms, as we...