How to: Machine Learning
_Tools that provide specific statistical algorithms for learning from data._

- [Apache Flink](https://flink.apache.org) - Fast, reliable, large-scale data processing engine.
- [Apache Mahout](https://mahout.apache.org) - Scalable algorithms focused on collaborative filtering, clustering and classification.
- [DatumBox](http://www.datumbox.com) - Provides several algorithms and pre-trained models for natural language processing.
- [Deeplearning4j](https://deeplearning4j.org) - Distributed and multi-threaded deep learning library.
- [DJL](https://djl.ai) - High-level and engine-agnostic framework for deep learning.
- [H2O ![c]](https://www.h2o.ai) - Analytics engine for statistics over big data.
- [Intelligent java](https://github.com/Barqawiz/IntelliJava) - Seamlessly integrate with remote deep learning and language models programmatically.
- [JSAT](https://github.com/EdwardRaff/JSAT) - Algorithms for pre-processing, classification, regression, and clustering with support for multi-threaded execution. (GPL-3.0-only)
- [m2cgen](https://github.com/BayesWitnesses/m2cgen) - CLI tool to transpile models into native code.
- [Neureka](https://github.com/Gleethos/neureka) - A lightweight, platform independent, OpenCL accelerated nd-array/tensor library.
- [oj! Algorithms](https://www.ojalgo.org/) - High-performance mathematics, linear algebra and optimisation needed for data science, machine learning and scientific computing.
- [Oryx 2](https://github.com/OryxProject/oryx) - Framework for building real-time, large-scale machine learning applications. Includes end-to-end applications for collaborative filtering, classification, regression, and clustering.
- [Siddhi](https://github.com/siddhi-io/siddhi) - Cloud native streaming and complex event processing engine.
- [Smile](https://github.com/haifengl/smile) - Statistical Machine Intelligence and Learning Engine provides a set of machine learning algorithms and a visualization library.
- [Tribuo](https://tribuo.org/) - Provides tools for classification, regression, clustering, model development and interfaces with other libraries such as scikit-learn, pytorch and TensorFlow.
- [Weka](https://www.cs.waikato.ac.nz/ml/weka/) - Collection of algorithms for data mining tasks ranging from pre-processing to visualization. (GPL-3.0-only)