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

Summary


Supervised learning is the predominant technique used in machine learning applications. The methodology consists of a series of steps beginning with data exploration, data transformation, and data sampling, through feature reduction, model building, and ultimately, model assessment and comparison. Each step of the process involves some decision making which must answer key questions: How should we impute missing values? What data sampling strategy should we use? What is the most appropriate algorithm given the amount of noise in the dataset and the prescribed goal of interpretability? This chapter demonstrated the application of these processes and techniques to a real-world problem—the classification problem using the UCI Horse Colic dataset.

Whether the problem is one of classification, when the target is a categorical value, or Regression, when it is a real-valued continuous variable, the methodology used for supervised learning is similar. In this chapter, we have used classification...