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

What are the characteristics of Big Data?


There are many characteristics of Big Data that are different than normal data. Here we highlight them as four Vs that characterize Big Data. Each of these makes it necessary to use specialized tools, frameworks, and algorithms for data acquisition, storage, processing, and analytics:

  • Volume: One of the characteristic of Big Data is the size of the content, structured or unstructured, which doesn't fit the storage capacity or processing power available on a single machine and therefore needs multiple machines.

  • Velocity: Another characteristic of Big Data is the rate at which the content is generated, which contributes to volume but needs to be handled in a time sensitive manner. Social media content and IoT sensor information are the best examples of high velocity Big Data.

  • Variety: This generally refers to multiple formats in which data exists, that is, structured, semi-structured, and unstructured and furthermore, each of them has different forms...