The fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
A powerful and flexible open-source data analysis and manipulation tool, built on top of the Python programming language. It offers data structures like Series and DataFrame that are designed for easy handling of structured data.
A comprehensive library for creating static, animated, and interactive visualizations in Python. It's a robust platform for building publication-quality figures and plots.
A data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics, making it easier to explore and understand data.
A simple and efficient tools for predictive data analysis. It features various classification, regression, clustering, dimensionality reduction, model selection, and preprocessing algorithms.
An end-to-end open-source platform for machine learning. It has a flexible architecture that allows researchers to experiment with the latest ideas in ML and helps developers to easily build and deploy ML-powered applications.
An open-source machine learning framework that accelerates the path from research prototyping to production deployment. It offers flexibility and speed, with a strong focus on deep learning research.
A high-level, user-friendly API for building and training neural networks. Designed for rapid experimentation, it allows for intuitive model definition and seamless integration with backends like TensorFlow.