Python Machine Learning Libraries

A versatile library for traditional ML algorithms, including classification, regression, and clustering.

An open-source framework for building deep learning models, especially neural networks.

Known for its dynamic computation graph, it's widely used in research for deep learning applications.

A high-level API for building and training deep learning models, often running on top of TensorFlow or Theano.

An optimized gradient boosting library designed for speed and performance in ML tasks.

Essential for data manipulation and analysis, offering data structures like DataFrames.

Fundamental for numerical computing in Python, providing support for large multi-dimensional arrays and matrices.

A fast DataFrame library optimized for large datasets with lazy evaluation.

The foundational plotting library in Python, useful for creating static, animated, and interactive visualizations.

Built on Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.

A comprehensive library for working with human language data.

An industrial-strength NLP library designed for performance and ease of use.

A powerful library focused on real-time computer vision tasks.

The Python Imaging Library (PIL) fork that adds image processing capabilities.

A numerical computation library that allows the definition and evaluation of mathematical expressions involving multi-dimensional arrays.

A high-level library built on PyTorch that simplifies training neural networks.

A toolkit for developing and comparing reinforcement learning algorithms.

A set of reliable implementations of reinforcement learning algorithms.

Provides visualization tools to help debug machine learning models.

An open-source low-code ML library that automates the ML workflow.

A gradient boosting framework that uses tree-based learning algorithms, known for its efficiency and speed.

A library for parsing HTML and XML documents, useful in web scraping tasks.

An open-source web-crawling framework for extracting the data you need from websites.

A lightweight WSGI web application framework that can be used to deploy ML models as web services.

An open-source app framework specifically designed for machine learning projects to create interactive web applications.

Provides classes and functions for estimating statistical models.

Enables parallel computing with task scheduling, particularly useful for large datasets.

An open-source platform designed to make machine learning accessible to everyone.

An automated machine learning tool that optimizes ML pipelines using genetic programming.

A gradient boosting library that handles categorical features automatically.

A data pipeline framework designed to manage datasets efficiently during training.