If you are a Java programmer and seeking to get a head start in or practice machine learning?

Authoring programs that leverage machine learning is the ideal way to learn machine learning. You can author the algorithms yourself from the ground up, but you can make a ton more progression if you harness an existing open source library.

In this blog article by AICorespot you will find out the major platforms and open source machine learning libraries you can leverage in Java.

Environments

This section details Java-based environments or workbenches that can be leveraged for machine learning. They are referred to as environments as they furnished graphical user interfaces for performance of machine learning activities, but also furnished Java APIs for generating your own applications.

Weka

Waikato Environment for Knowledge Analysis (Weka) is a machine learning platform produced by the University of Waikato, New Zealand. It is authored in Java and furnishes a graphical user interface, command line interface and Java API. It is probably the most widespread Java machine learning library and a brilliant place to begin or practice machine learning.

KNIME

The Konstanz Information Miner (KIME) is an analytics and reporting platform produced by Konstanz University, Germany. It was produced with a focus on pharmaceutical research, but has expanded into general business intelligence. It furnishes a graphical user interface on the basis of Eclipse and a Java API.

RapidMiner

RapidMinder was previously referred to as Yet Another Learning Environment (YALE) and was produced at Technical University of Dortmund, Germany. It furnishes a GUI and a Java API for producing your own applications. It furnishes data handling, visualization, and modelling with machine learning algorithms.

ELKI

The Environment for Developing KDD-Applications Supported by Index Structures (ELKI) is a data mining workbench generated in Java by the Ludwig Maximillian University of Munich, Germany. It has a concentration on operating with data in relational database for activities like outlier identification and classification (distance function based strategies). It furnishes a mini GUI, command line interface and Java API.

Libraries

Literally every project detailed on this page is/has a library with a Java API, those projects detailed in this section only furnish a Java API. They are machine learning libraries in the narrow sense.

Java-ML

The Java Machine Learning Library (Java-ML) furnishes a collection of machine learning algorithms that have implementation in Java. It furnishes a standard interface for every algorithm, no UIs and references to the appropriate scientific literature for further reading. It consists of strategies for data manipulation, clustering, feature selection, and classification. Observe that at the time of writing, the final release was way back in 2012.

JSAT

The Java Statistical Analysis Tool (JSAT) furnishes pure Java implementations of conventional machine learning algorithms for modest sized problems. The writer comments that he produced the library partially as a self-education exercise and partially to get things completed. Nonetheless, the listing of algorithms is impressive. It consists of classification, regression, ensemble, clustering and feature selection methods.

Big Data

This section details Java projects intended for leveraging with Big Data, like on clusters of machines.

Mahout (Hadoop)

Apache Mahout furnishes implementations of machine learning algorithms for leveraging on the Apache Hadoop platform (distributed map-reduce). The project furnishes a concentration on clustering and classification algorithms and a widespread application driving implementation is its leveraging in collaborative filtering for recommender systems. Reference implementations of algorithms that run on a singular node are also included.

MLlib (Spark)

Apache Machine Learning Library furnishes implementations of machine learning algorithms for leveraging on the Apache Spark platform (HDFS, but not map-reduce). Even though Java, the library and the platform support Java, Scala and Python bindings. The library is fresh and the listing of algorithms is short, but growing quickly.

MOA

Massive Online Analysis (MOA) is an open source platform developed for data stream mining by University of Waikato, New Zealand. Like Weka (developed at the same place), it furnishes a GUI, command line interface and Java API. It furnishes a protracted listing of algorithms with a concentration on classification and support for outlier detection and tackling concept drift. MOA leverages the Advanced Data mining and Machine Learning System (ADAMS) for handling workflows also produced at the same place.

SAMOA

Scalable Advanced Massive Online Analysis (SAMOA) is a distributed streaming machine learning framework produced by Yahoo!. It is developed to run on Apache Storm and Apache S4. The system can harness the algorithms furnished by the MOA project for activities such as classification.

Natural Language Processing

This portion of the blog is devoted to Java libraries and projects for tackling problems from the subdomain of machine learning referred to as Natural Language Processing (NLP)

The following are some critical libraries with regards to NLP.

• OpenNLP: Apache OpenNLP is a toolkit for process of natural language text. It furnishes strategies for NLP activities like tokenization, segmentation, and entity extraction.
• LingPipe: LingPipe is a toolkit for computational linguistics and consists of strategies for topic classification, entity extraction, clustering, and sentiment analysis.
• GATE: The General Architecture for Text Engineering (GATE) is an open source library for text process. It furnishes an array of sub-projects targeted at differing use cases.
• MALLET: Machine Learning for Language Toolkit (MALLET) is a Java toolkit for statistical natural language processing, document classification, clustering, topic modelling and information extraction.

Computer Vision

This section details those libraries for the subdomain of machine learning referred to as Computer Vision (CV).

The following are some of the critical CV libraries:

• BoofCV: BoofCV is an open-source library for computer vision and robotics applications. It assists features like image processing, features, geometric vision, calibration, recognition, and image data IO.

Deep Learning

Neural Nets are trending again with the development of deep learning strategies and quicker hardware. This section details critical Java libraries for operating with neural networks and deep learning.

• Encog: Encog is a machine learning library that furnishes algorithms like SVM, conventional neural networks, genetic programming, Bayesian networks, HMM and genetic algorithms.
• Deeplearning4j: Deeplearning4j is stated to be a commercial grade deep learning library authored in Java. It is detailed as being compatible with Hadoop and furnishes algorithms which includes Restricted Boltzmann machines, deep-belief networks and Stacked Denoising Autoencoders.

Conclusion

In this summarization post we have touched on the big name options when choosing a library or platform for machine learning when operating in Java.

These are the players and the widespread projects, but by no means is this listing comprehensive. For instance, take a look at MLOSS.org that lists 71 Java-based open source machine learning projects. We’re sure there are more available on GitHub and SourceForge.

The key is to contemplate hard about your own project and it’s requirements. Find out what you require from a library or platform and then choose and learn a project that ideally fits your requirements.