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 case study, we use the CoverType dataset to demonstrate classification and clustering algorithms from H2O, Apache Spark MLlib, and SAMOA Machine Learning libraries in Java.

Business problem

The CoverType dataset available from the UCI machine learning repository (https://archive.ics.uci.edu/ml/datasets/Covertype) contains unscaled cartographic data for 581,012 cells of forest land 30 x 30 m2 in dimension, accompanied by actual forest cover type labels. In the experiments conducted here, we use the normalized version of the data. Including one-hot encoding of two categorical types, there are a total of 54 attributes in each row.

Machine Learning mapping

First, we treat the problem as one of classification using the labels included in the dataset and perform several supervised learning experiments. With the models generated, we make predictions about the forest cover type of an unseen held out test dataset. For the clustering experiments that follow, we ignore the data labels...