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What are the libraries used in Machine Learning?

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Libraries are collections of precompiled routines used by programs. They are extremely useful in order to store used routines so that there is no need for you to link them to every program explicitly. They make it easy for developers to perform complex tasks without having to rewrite the same lines of codes over and over.

A large part of Machine Learning is based on mathematics. It basically performs operations based on mathematical optimization, probability, and statistics. The python libraries help the machines to develop codes without having to rewrite similar codes repeatedly.

Machine Learning uses various Python data visualization libraries including:

  • Numpy: It stands for Numerical Python. This library is used in order to create arrays in Python. It also provides tools for integrating programming languages like C and C++.
  • Panda: It is an open-source library that is built on top of Numpy to operate. It allows you to easily create, and manipulate the data.
  • Matplotlib: It is a plotting library for Python. It provides object-oriented API to embed plots in applications with the help of GUI toolkits such as Tkinter, Qt, or wxPython. 
  • Seaborn: It is a data visualization library based on Matplotlib. It provides a good interface to draw informative statistical graphics.
  • Geoplotlib: It is used to create maps and plot geographical data. You need to install Pyglet to make use of this library.
  • Plotly: It allows users to create interactive web-based visualizations that can be displayed in Jupyter notebooks. 

To learn in detail about the numerous libraries used in Machine Learning, watch this YouTube video:

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