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scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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OpenCV is the world's biggest computer vision library.
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Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
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NumPy is the fundamental package for scientific computing with Python.
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Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
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MLKit is a simple machine learning framework written in Swift.
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DatumBox is a machine learning framework that can be applied to a wide variety of complex applications, though it's primarily used for social media analytics.
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Pylearn2 is a library for machine learning research.
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ml.js is a machine learning and numeric analysis tools in javascript for node.js and browser.
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ITK is an open-source, cross-platform library that provides developers with an extensive suite of software tools for image analysis.
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PRMLT package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C.
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Apache SystemML is a machine learning platform optimal for big data.
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Application and Data, Assets and Media, and Image Processing and Management.
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CloudForest allows multi-threaded ensembles of decision trees for machine learning in pure Go.
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Feature Forge offers a set of tools for creating and testing machine learning features.
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Recommender is C library for product recommendations/suggestions using collaborative filtering (CF).
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MLDB is an open-source database designed for machine learning that can be install in any device and send commands over a RESTful API to store data.
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SwiftLearner is a scala machine learning library that is easier to follow than the optimized libraries, and easier to tweak it use plain Java types and have few or no dependencies.
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haskell-ml is Haskell implementations of various ML algorithms.
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rustlearn is a machine learning crate for Rust that contains reasonably effective implementations of a number of common machine learing algorithms.