Book Image

Java Data Science Cookbook

By : Shams
Book Image

Java Data Science Cookbook

By: Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


In Chapter 4, Learn from Data - Part 1, we used the Weka machine learning workbench for different recipes for classification, clustering, association rule mining, feature selection, and so on. We also mentioned in that chapter that Weka is not the only tool that is written in Java to learn patterns from data. There are other tools that can perform similar tasks. Examples of such tools include but are not limited to Java Machine Learning (Java-ML) library, Massive Online Analysis (MOA), and Stanford machine learning libraries.

In this chapter, we will be focusing on bits and pieces of these other tools to conduct machine learning analysis on data.