machine learning tools

What is Machine learning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications o become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.

Types of machine learning

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are 4 basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm a data scientist chooses to use depends on what type of data they want to predict.

  1. Supervised learning. In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
  2. Unsupervised learning. This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. Both the data algorithms train on and the predictions or recommendations they output are predetermined.
  3. Semi-supervised learning. This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
  4. Reinforcement learning. Reinforcement learning is typically used to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

This article is intended not only for Java web developers. Business owners need to know whether a programmer can develop ML applications efficiently, which includes familiarity with machine learning tools packages in Java. Moreover, if you have a say in the tech stack discussions, it’s useful to know the context.

The focus on Java machine learning reflects the popularity of the language. Due to its extreme stability, leading organizations and enterprises have been adopting Java for decades.

This is a list of 25 Java Machine learning tools & libraries. The students with technical background can train on latest Java technologies to get your desired job as companies are offering jobs for Java developers with huge package.

1. Weka

Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.

2. Meka

MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the ‘standard’ case which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit.

3. Advanced Data mining And Machine learning System

Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows, released under GPLv3.


Environment for Developing KDD-Applications Supported by Index-Structure (ELKI) is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.

5. Mallet

Mallet is a java machine learning toolkit for  textual document. Mallet supports classification algorithms like maximum entropy, naive bayes and decision tree for classification.

6. Encog

Encog is an advanced machine learning framework which supports Support Vector Machines,Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported.

7. Massive Online Analysis

Massive Online Analysis (MOA) is a popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.

8. Datumbox

Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development Machine Learning and Statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical tests and being able to handle medium-large sized datasets.

9. DL4J

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. It is designed to be used in business environments, rather than as a research tool.

10 Mahout

Mahout is a machine learning framework with built in algorithms. Mahout-Samsara helps people create their own math while providing some off-the-shelf algorithm implementations.

11. Rapid Miner

Rapid Miner was developed at Technical University of Dortmund, Germany. It provides a GUI and a Java API for developing your own applications. It provides data handling, visualization and modeling with machine learning algorithms.

12. Apache SAMOA

Apache SAMOA is a machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms and enables development of new ML algorithms without directly dealing with the complexity of underlying distributed stream processing engines (DSPEe, such as Apache Storm, Apache S4, and Apache Samza). Its users can develop distributed streaming ML algorithms once and execute them on multiple DSPEs.

13. Neuroph

Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.

14. Oryx 2 

Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning. It is a framework for building applications, but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering.

15. Stanford Classifier

Stanford Classifier is a machine learning tool that will take data items and place them into one  of k classes. A probabilistic classifier, like this one, can also give a  probability distribution over the class assignment for a data item. This  software is a Java implementation of a maximum entropy classifier.

16. is a Retina API fast, precise and brain like algorithm that enables NLP.

17. JSAT

JSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such – all code is self contained. JSAT has no external dependencies, and is pure Java.


KNIME is a tool for data analytics, reporting and integration platform. Using the data pipelining concept, it combines different components for machine learning and data mining.

19. Java Machine Learning Library

Java Machine Learning Library is a set of reference implementations of machine learning algorithms. These algorithms are well documented, both in the source code as on the documentation site.It is mostly written in Java.

20. Java-ML

Java-ML is a Java API with a collection of machine learning algorithms implemented in Java. It only provides a standard interface for algorithms.

21. MLlib

MLlib (Spark) is Apache Spark’s scalable machine learning library. Although Java, the library and the platform support Java, Scala and Python bindings. The library is new and the list of algorithms is long.

22. H2O

H2O  is a machine learning API for smarter applications. It scales statistics, machine learning, and math over big data. H2O is extensible and individual can build blocks using simple math legos in the core.

23. Keras 

Keras is an API for neural networks. It helps in doing quick research and is written in Python.

24. RankLib

RankLib is a library of learning to rank algorithms. Currently eight popular algorithms have been implemented.

25. (Hierarchical Temporal Memory implementation in Java) is a Java port of the Numenta Platform for Intelligent Computing. Source