Python libraries provide us with useful functions for writing code without having to start from scratch. You can create applications and models with Python's 137,000+ libraries.
Pandas is a BSD licensed open source library widely used in the field of data science. They are primarily used for data analysis, manipulation, cleaning, etc.
Among the most widely used open-source Python libraries for scientific computation. It supports multidimensional data and has built-in mathematical functions for quick calculations.
A Python-based neural network library, Keras allows us to quickly experiment with deep neural networks. It emerges as a great option as it emerges as a human-centric API.
A high-performance numerical calculation library, TensorFlow is used in deep learning and ML algorithms. Math, physics, and ML researchers use it for complex calculations.
Scikit Learn is an open-source library for ML algorithms that runs in the Python environment. It can be used with both supervised and unsupervised learning algorithms.
Eli5 Python ML library addresses the difficulty of inaccurate ML predictions. In addition to visualization and debugging, it also tracks the algorithms' working process.
Scipy is used for scientific computation, data processing, and high-performance computing. For quick computations, the library contains a large number of user-friendly routines.