ML Open Source Development

ML Open Source Development at Nestack enables machine learning, image processing, neural networks, and more.

ML Open Source Development
ML Open Source Development

Machine learning libraries

Machine learning libraries are essential tools for data scientists and developers, offering a wide range of algorithms and tools for data mining, analysis, and model building.


Scikit-learn is a machine learning project that offers Python tools to handle data mining and analysis via algorithms for classification, clustering, regression, dimensionality reduction, and more. Scikit-learn is based on SciPy, NumPy, and matplotlib.


OS: Windows, Linux, macOS
Shogun began development in 1999 and has grown into a set of machine learning tools which can support Python, R, Java/Scala, Lua, C#, Ruby, Octave, and various other programming languages. Shogun also offers a free cloud service for users to play around with the software.

Distributed Machine Learning Toolkit

OS: Windows, Linux
This machine learning project is a Microsoft brainchild which includes the Light LDA topic model algorithm, the DMTK Framework, the Distributed (Multisense) Word Embedding algorithm, and the LightGBM gradient boosting tree framework. There are more algorithms and components planned for this toolkit which will all be updated as the company’s research progresses.


OS: Windows, Linux, macOS
Encog is the brainchild of data scientist Jeff Heaton, a machine learning framework which has been developing in an ongoing fashion since 2008. Encog supports neural and Bayesian networks, hidden Markov models, support vector engines, and genetic programming and algorithms.


OS: Linux, macOS
GoLearn is a simple and easily customizable “batteries included” machine learning library that operates with the Go programming language.


OS: Windows, Linux, macOS
PredictionIO is a machine-learning server that is currently an Apache incubating project. It can integrate with various other open-source tools such as MLib, HBase, Spray, Spark, and Elasticsearch, and comes with customizable templates and real-time query response. It can also helpfully ingest data from multiple platforms.


OS: Windows, Linux, macOS
Shark is the so-called “fast, modular, feature-reich open-source C++ machine learning library” that can provide algorithms for optimization, evolutionary algorithms, basic linear algebra, and supervised and unsupervised learning.


OS: Windows, Linux, macOS
The Statistical Machine Intelligence and Learning Engine, or Smile, is a particularly quick machine learning option for Java, Scala, and various other JVM languages which claims to outperform Python, R, Spark, H2O, and xgboost by a significant portion.


OS: Windows, Linux, macOS.
Dlib provides a quick method of working within several C++ machine learning libraries. There are a host of features including algorithms for multiclass classification, regression, binary classification, clustering, deep learning, unsupervised learning, semi-supervised/metric learning, feature selection, and reinforcement learning. It also provides anthropometric data such as facial landmarks and features which are widely used in robotics to enhance human interaction.


OS: Windows,Linux,macOS
Opencv is a widely used computer vision library written originally in C++. It also has its python bindings which increases its value globally and exponentially. Opencv implements complex machine learning algorithms for object detection and recognition. Machine learning is playing a vital role in boosting computer graphics.

Oryx 2

OS: Windows, Linux, macOS
Oryx 2 is a Cloudera creation based on Kafka and Apache Spark which implements lambda architecture for machine learning.


OS: Linux
Scalable Advanced Massive Online Analysis, or SAMOA, is an Apache incubating projects and machine learning framework for use with distributed streaming applications.

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