Machine learning is a mixture of mathematical optimization and statistics, each tutorial disciplines in their very own right. Machine learning is presently driving one thing of a recognition wave. Machine learning is part of computer science, and therefore its practitioners are extremely skilled computer programmers. That being said, let’s highlight 5 Best machine learning libraries for Java.

1 - Weka

Weka is 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. It is also well-suited for developing new machine learning schemes.


Why The Best

  • It has 4 modes - GUI, command line, Experimenter , Knowledge Flow.
  • It has implementations of most of machine learning algorithms.
  • It lets you run learning algorithms on text files.
  • Weka is open source and has a decent documentation.
  • You can extend it using your code.
  • It has advanced features to support setting up long running mining runs as well as experiment and compare various algorithms.

2 - Rapid miner

RapidMiner offers a suite of products that allow data analysts to build new data mining processes, set up predictive analysis, and more. The list of products include: RapidMiner Studio, RapidMiner Server, RapidMiner Radoop, and RapidMiner Streams.


Why The Best

  • RapidMiner includes a lot of Machine Learning libraries and algorithms.
  • It is really easy to construct a machine learning workflow
  • It includes loading data, features selection and cleaning, applying machine learning models and visualization.
  • Makes Easy to construct simple and understandable machine learning workflows

3 - Massive Online Analysis (MOA)

MOA is the most 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.


Why The Best

  • moa performs big data stream mining in real time
  • It performs large scale machine learning.
  • moa can be extended with new mining algorithms
  • moa can be extended with new stream generators or evaluation measures
  • It provides a benchmark suite for the stream mining community.

4 - Deeplearning4j

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala.Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.Skymind is its commercial support arm.


Why The Best

  • DL4J takes advantage of the latest distributed computing frameworks.
  • It includesHadoop and Apache Spark to accelerate training.
  • Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure or Kotlin.
  • The libraries are completely open-source.

5 - ELKI

ELKI is an open source data mining software written in Java. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions of additional methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.


Why The Best

  • In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation
  • ELKI is open to arbitrary data types, distance or similarity measures, or file formats.
  • The fundamental approach is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms.