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

Issues in common with supervised learning


Many of the issues that we discussed related to supervised learning are also common with unsupervised learning. Some of them are listed here:

  • Types of features handled by the algorithm: Most clustering and outlier algorithms need numeric representation to work effectively. Transforming categorical or ordinal data has to be done carefully

  • Curse of dimensionality: Having a large number of features results in sparse spaces and affects the performance of clustering algorithms. Some option must be chosen to suitably reduce dimensionality—either feature selection where only a subset of the most relevant features are retained, or feature extraction, which transforms the feature space into a new set of principal variables of a lower dimensional space

  • Scalability in memory and training time: Many unsupervised learning algorithms cannot scale up to more than a few thousands of instances either due to memory or training time constraints

  • Outliers and noise in...