10 Best Python Libraries for Machine Learning & AI

TensorFlow: An open-source library for machine learning developed by Google that allows for the deployment of computations to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn: A library for machine learning in Python, providing a wide range of algorithms for classification, regression, and clustering.
Keras: An open-source library for building neural networks in Python, which runs on top of TensorFlow, Theano, and CNTK.
PyTorch: An open-source machine learning library for Python, developed by Facebook, that provides a wide range of tools for building and training neural networks.
XGBoost: An optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM: A gradient boosting framework that uses tree-based learning algorithms.
CatBoost: A gradient boosting library developed by Yandex that uses categorical features by default without the need to preprocess them.
Scikit-optimize: A library for optimization using different techniques such as Bayesian optimization, evolutionary optimization, and gradient-based optimization.
MLxtend: A library of useful tools for machine learning, including feature selection, model evaluation, and ensemble learning.
PyBrain: A flexible and easy-to-use library for creating neural networks and other machine learning algorithms in Python.