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 – Horse Colic Classification


To illustrate the different steps and methodologies described in Chapter 1, Machine Learning Review, from data analysis to model evaluation, a representative dataset that has real-world characteristics is essential.

We have chosen "Horse Colic Dataset" from the UCI Repository available at the following link: https://archive.ics.uci.edu/ml/datasets/Horse+Colic

The dataset has 23 features and has a good mix of categorical and continuous features. It has a large number of features and instances with missing values, hence understanding how to replace these missing values and using it in modeling is made more practical in this treatment. The large number of missing data (30%) is in fact a notable feature of this dataset. The data consists of attributes that are continuous, as well as nominal in type. Also, the presence of self-predictors makes working with this dataset instructive from a practical standpoint.

The goal of the exercise is to apply the techniques...