9 Machine Learning Tools You Should Know About

9 Machine Learning Tools You Should Know About -magoosh

Machine learning is a very sought-after Computer Science branch and more and more people are willing to learn it. In this article, we mention 9 Machine Learning tools to make learning ML easier.


The Technical University of Dortmund, Germany developed RapidMiner. It provides a graphical interface and a Java API to develop your own applications. It provides data processing, visualization and modeling of machine learning algorithms.


WEKA: It has a wide collection of algorithms for learning how to do data mining tasks. Algorithms can be directly applied to a dataset or from one’s own Java code. Pre-processing data, categorization, regression, clustering, association rules, and vision can be done by the tools that WEKA includes.


H2O is a ML API for smart and complex applications. It scales statistics, learning, and mathematics despite large volumes of data. H2O is flexible and one can develop blocks by using simple mathematical tools in the nucleus.

Massive Online Analysis (MOA)

MOA is a famous alternative framework for collecting data streams, with an increasingly active community. It contains a collection of learning algorithms and assessment tools. Connected with WEKA project, it is also written in Java, while deducting more challenging problems.


It is an autonomous software (AGPLv3), which is a data mining software that is written in Java. The goal of the ELKI research is in algorithms, with a focus on unattended methods in cluster analysis and outlier detection.


JSAT is a library to quickly start with machine learning problems. Under GPL 3, JSAT is made available for use. Part of the library is for learning in itself, as such – all code is standalone. It is a pure form of Java which has no external dependencies.

Apache SAMOA

SAMOA consists of programming abstraction for machine learning distributed streaming algorithms and facilitates development of new learning machine algorithms. There is no need to deal with the difficulty of underlying layers such as distributed flow processing engine (DFPEe, Apache Samza, Storm, Apache S4). Users can create distributed ML streaming algorithms which can be started and run on multiple DSPEs.

MLlib (Spark)

MLlib (Spark) is a scalable learning library of Spark Apache. Although Java, the library has the support of the Java Platform and Scala Python come together. The list of algorithms is long for the new library.


A Java API, with a bundle of ML algorithms implemented in Java forms Java-ML. It does not specify any standard interface for algorithms.

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