Machine Learning in Java by Bostjan Kaluza

Machine Learning in Java



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Machine Learning in Java Bostjan Kaluza ebook
ISBN: 9781784396589
Format: pdf
Publisher: Packt Publishing, Limited
Page: 227


This method will return the score for the supplied feature index. Most machine learning books are very theoretical. There are two ways to get the source code. If your dataset is massive and you need Hadoop (e.g. You will employ machine learning algorithms to improve a new layer of intelligence in NewsWhip's platform. A clustering algorithm creates a division of the orginal dataset. If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. The Java Data Mining Package is a library for Machine Learning and Big Data Analytics with support for classification, clustering, and much more. This document covers the very basic documentation of the library. –� Basic introduction to Machine Learning. Typical usage of a feature scoring algorithm is shown in the snippet below. Your data is on HDFS), you may want to go the Java route for Mahout [1]. Weka has a collection of machine learning algorithms for data mining tasks. Apache Hadoop is written in java, so is Apache Mahout a fairly comprehensive machine learning library on top of hadoop. Very few books explain how to use machine learning in practical manner, especially for Java developers. There are number of machine learning Java libraries available to help build smart data-driven applications. This is a list of 25 Java Machine learning tools & libraries. 29 Case Studies: Java Machine Learning. In Java-ML this is done with the method cluster of the Clusterer interface. The Java Machine Learning Library readme documentation.





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