How to: Machine Learning
* [AI4R](https://github.com/sergiofierens/ai4r) - Algorithms covering several Artificial intelligence fields.
* [Awesome Machine Learning with Ruby](https://github.com/arbox/machine-learning-with-ruby) - A Curated List of Ruby Machine Learning Links and Resources.
* [langchain.rb](https://github.com/patterns-ai-core/langchainrb) - Library for building LLM-powered applications in Ruby.
* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A CLI tool to transpile trained classic ML models into a native Ruby code with zero dependencies.
* [PredictionIO Ruby SDK](https://github.com/PredictionIO/PredictionIO-Ruby-SDK) - The PredictionIO Ruby SDK provides a convenient API to quickly record your users' behavior and retrieve personalized predictions for them.
* [rb-libsvm](https://github.com/febeling/rb-libsvm) - Ruby language bindings for LIBSVM. SVM is a machine learning and classification algorithm.
* [ruby-fann](https://github.com/tangledpath/ruby-fann) - Ruby library for interfacing with FANN (Fast Artificial Neural Network).
* [ruby-openai](https://github.com/alexrudall/ruby-openai) - OpenAI API + Ruby!
* [rumale](https://github.com/yoshoku/rumale) - A machine learning library with interfaces similar to Scikit-Learn.
* [TensorFlow](https://github.com/ankane/tensorflow) - The end-to-end machine learning platform for Ruby.
* [Torch.rb](https://github.com/ankane/torch.rb) - Deep learning for Ruby, powered by LibTorch.
* [weka](https://github.com/paulgoetze/weka-jruby) - Machine learning and data mining algorithms for JRuby.