Machine Learning in Java. Bostjan Kaluza

Machine Learning in Java


Machine.Learning.in.Java.pdf
ISBN: 9781784396589 | 227 pages | 6 Mb


Download Machine Learning in Java



Machine Learning in Java Bostjan Kaluza
Publisher: Packt Publishing, Limited



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 language of data science with support for R, Python, Scala, Java and a robust REST API. One of the most well-known repositories of machine learning related datasets is the UCI Machine Learning Repository. Weka is a collection of machine learning algorithms for solving real- world data mining problems. MLTK is a collection of various supervised machine learning algorithms, which is designed for directly training models and further developement. Software Development Engineer - Machine Learning data normalization, search relevance, recommendation systems, and machine learning. Download the second version if you already have Java 1.6 (or later) on your system. The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. There are number of machine learning Java libraries available to help build smart data-driven applications. Package com.amazonaws.services.machinelearning AmazonMachineLearningAsync. MLlib machine learning library overview for Spark 1.5.2. H2O - Fast Scalable Machine Learning For Smart Applications. This document covers the very basic documentation of the library. These executables will install Weka in your Program Menu. The Java Machine Learning Library readme documentation. Interface for accessing Amazon Machine Learning asynchronously. A clustering algorithm creates a division of the orginal dataset. Machine-learning-samples - Sample applications built using Amazon Machine Learning. (For full disclosure: I'm also the author of a Java deep learning library, available here, and the examples in this article are implemented using the above library. In Java-ML this is done with the method cluster of the Clusterer interface. For an overview of the techniques implemented in Weka, and the software itself, you may want to consider taking a look at the data mining book.





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