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

Big Data Machine Learning


In this section, we will discuss the general flow and components that are required for Big Data Machine Learning. Although many of the components, such as data acquisition or storage, are not directly related to Machine Learning methodologies, they inevitably have an impact on the frameworks and processes. Giving a complete catalog of the available components and tools is beyond the scope of this book, but we will discuss the general responsibilities of the tasks involved and give some information on the techniques and tools available to accomplish them.

General Big Data framework

The general Big Data framework is illustrated in the following figure:

Figure 1: Big data framework

The choice of how the Big Data framework is set up and deployed in the cluster is one of the decisions that affects the choice of tools, techniques, and cost. The data acquisition or collection component is the first step and it consists of several techniques, both synchronous and asynchronous...