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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. [3]
scikit-learn, a library for machine learning. TomoPy, a package for tomographic data processing and image reconstruction; Veusz, a scientific plotting package; VisTrails, a scientific workflow and provenance management software with visual programming interface and integrated visualization (via Matplotlib, VTK). Apache Singa, a library for deep ...
The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. After having worked for Silveregg, a SaaS Japanese company delivering recommendation systems for Japanese online retailers, [ 3 ] he worked for 6 years at Enthought , a scientific consulting company.
Windows 8/7/Vista/XP/2000 Note: Downloading and installing of Java will only work in Desktop mode on Windows 8. If you are using the Start screen, you will have to switch it to Desktop screen to run Java. Windows Server 2008/2003; Intel and 100% compatible processors are supported; Pentium 166 MHz or faster processor with at least 64 MB of ...
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft.
It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3] Development of the original project, known as PIL, was discontinued in 2011. [2] Subsequently, a successor project named Pillow forked the PIL repository and added Python 3.x support. [5]