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[RubyNLP | RubyDataScience | RubyInterop]

Awesome Machine Learning with Ruby Awesome

A Curated List of Ruby Machine Learning Links and Resources

Machine Learning is a field of Computational Science - often nested under AI research - with many practical applications due to the ability of resulting algorithms to systematically implement a specific solution without explicit programmer's instructions. Obviously many algorithms need a definition of features to look at or a biggish training set of data to derive the solution from.

This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.

A lot of useful resources on this list come from the development by The Ruby Science Foundation, our contributors and our own day to day work on various ML applications. Read why this list is awesome.

Every contribution is welcome! Add links through pull requests or create an issue to start a discussion.

Follow us on Twitter and please spread the word using the #RubyML hash tag!



Please help us to fill out this section! 😃

Machine Learning Libraries

Machine Learning algorithms in pure Ruby or written in other programming languages with appropriate bindings for Ruby.


  • weka - JRuby bindings for Weka, different ML algorithms implemented through Weka.
  • ai4r - Artificial Intelligence for Ruby.
  • classifier-reborn - General classifier module to allow Bayesian and other types of classifications. [dep: GLS]
  • scoruby - Ruby scoring API for PMML (Predictive Model Markup Language).
  • rblearn - Feature Extraction and Crossvalidation library.
  • data_modeler - Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.
  • shogun - Polyfunctional and mature machine learning toolbox with Ruby bindings and enormous documentation.
  • aws-sdk-machinelearning - Machine Learning API of the Amazon Web Services.
  • azure_mgmt_machine_learning - Machine Learning API of the Microsoft Azure.
  • machine_learning_workbench - Growing machine learning framework written in pure Ruby, high performance computing using Numo, CUDA bindings through Cumo. Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty of examples and utilities.
  • Deep NeuroEvolution - Experimental setup based on the machine_learning_workbench towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the OpenAI Gym using PyCall.

Neural networks

  • neural-net-ruby - Neural network written in Ruby.
  • ruby-fann - Ruby bindings to the Fast Artificial Neural Network Library (FANN).
  • cerebrum - Experimental implementation for Artificial Neural Networks in Ruby.
  • tlearn-rb - Recurrent Neural Network library for Ruby.
  • brains - Feed-forward neural networks for JRuby based on brains.
  • machine_learning_workbench - Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks (fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms).
  • rann - Flexible Ruby ANN implementation with backprop (through-time, for recurrent nets), gradient checking, adagrad, and parallel batch execution.

Kernel methods

Evolutionary algorithms

  • machine_learning_workbench - Framework including pure-Ruby implementations of Natural Evolution Strategy algorithms (black-box optimization), specifically Exponential NES (XNES), Separable NES (sNES), Block-Diagonal NES (BDNES) and more. Applications include neural network search/training (neuroevolution).
  • simple_ga - Simplest Genetic Algorithms implementation in Ruby.

Bayesian methods

  • linnaeus - Redis-backed Bayesian classifier.
  • naive_bayes - Simple Naive Bayes classifier.
  • nbayes - Full-featured, Ruby implementation of Naive Bayes.

Decision trees


  • flann - Fast Library for Approximate Nearest Neighbors. [flann]
  • kmeans-clusterer - k-means clustering in Ruby.
  • k_means - Attempting to build a fast, memory efficient K-Means program.
  • knn - Simple K Nearest Neighbour Algorithm.

Linear classifiers

  • liblinear-ruby-swig - Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
  • liblinear-ruby - Ruby interface to LIBLINEAR using SWIG.

Statistical models

  • rtimbl - Memory based learners from the Timbl framework.
  • lda-ruby - Ruby implementation of the LDA (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.
  • maxent_string_classifier - JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
  • omnicat - Generalized rack framework for text classifications.
  • omnicat-bayes - Naive Bayes text classification implementation as an OmniCat classifier strategy. [dep: bundled]

Applications of machine learning

  • phashion - Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files. [ImageMagick | libjpeg]

Data structures

If you're going to implement your own ML algorithms you're probably interested in storing your feature sets efficiently. Look for appropriate data structures in our Data Science with Ruby list.

Data visualization

Please refer to the Data Visualization section on the Data Science with Ruby list.

Articles, Posts, Talks, and Presentations

Projects and Code Examples

  • Wine Clustering - Wine quality estimations clustered with different algorithms.
  • simple_ga - Basic (working) demo of Genetic Algorithms in Ruby.

Heroku buildpacks

Books, Blogs, Channels


Needs your Help!

All projects in this section are really important for the community but need more attention. Please if you have spare time and dedication spend some hours on the code here.

Related Resources

Wait but why?

There are a lot of software lists with ML related tools. There are a couple of lists with Ruby related projects. There are no lists of only working and tested software with documented scope. We'll try to make one!

What is awesome? Awesome are documented, maintained and focused tools.

Can something turn not awesome at a point? Yes! Abandoned projects with broken dependencies aren't awesome any more! They leave this list.


Creative Commons Zero 1.0 Awesome ML with Ruby by Andrei Beliankou and Contributors.

To the extent possible under law, the person who associated CC0 with Awesome ML with Ruby has waived all copyright and related or neighboring rights to Awesome ML with Ruby.

You should have received a copy of the CC0 legalcode along with this work. If not, see