What Makes Python Libraries So Important For Data Science Learning?

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/u_Snoo36930

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  • scikit-learn

    scikit-learn: machine learning in Python

    Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as Scikit-Learn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

  • Pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

    Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as Scikit-Learn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

  • OPS

    OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as Scikit-Learn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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