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The current development of the SciPy library is supported and sponsored by an open community of developers and distributed under the free BSD license. The resulting library was then named as SciPy library. Travis Oliphant, Eric Jones, and Pearu Peterson in 2001 decided to merge most of these bits and pieces codes and standardize it. With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. The key features of NumPy include powerful N-dimensional array object, broadcasting functions, and out-of-box tools to integrate C/C++ and Fortran code. NumPy can also serve as an efficient multi-dimensional container for any generic data that is in any datatype. With NumPy, you can define arbitrary data types and easily integrate with most databases. Other libraries like TensorFlow uses NumPy at the backend for manipulating tensors. NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers. An extensive collection of high complexity mathematical functions make NumPy powerful to process large multi-dimensional arrays and matrices. NumPy is a well known general-purpose array-processing package. Top Python Machine Learning Libraries 1) NumPy Is Python a fully object-oriented programming language?.
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