Data Science
Showing 36 of 243 projects
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A community-driven collection of data science interview questions and answers covering theory, technical skills, and probability.
A standardized, flexible project template for data science work using Cookiecutter to structure reproducible projects.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A GPU-accelerated DataFrame library for tabular data processing, part of the RAPIDS data science suite.
A modular deep learning library providing a higher-level API for TensorFlow to speed up experimentation.
A batteries-included machine learning library for Go with a scikit-learn inspired interface.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
Automatically extracts and selects relevant features from time series data for machine learning tasks.
A curated list of practical financial machine learning tools, applications, and research repositories.
A high-performance Python DataFrame library for lazy out-of-core processing and visualization of billion-row datasets at interactive speeds.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A multi-user server that spawns, manages, and proxies multiple instances of single-user Jupyter notebook servers.
A weekly social data project providing real-world datasets for practicing data tidying, visualization, and analysis.
Code and Jupyter notebooks for the book 'Introduction to Machine Learning with Python' by Andreas Mueller and Sarah Guido.
A comprehensive cheat sheet with classical equations and diagrams for machine learning knowledge recall and interview preparation.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
An open-source Python framework to evaluate, test, and monitor ML and LLM systems with 100+ built-in metrics.
A cross-platform, language-agnostic binary package and environment manager for creating isolated software environments.
A curated collection of tutorials and resources following the data scientist roadmap for learning essential data science skills.
Convert Jupyter notebooks to and from plain text formats like Markdown, Python, Julia, or R scripts for better version control and editing.
A collection of Jupyter notebooks with real-world examples for learning Python's pandas data analysis library.
An open-source feature store for managing and serving machine learning features for training and online inference.
A collection of over 230 pure-Python utilities that extend the standard library with missing functionality.
An open-source Python package for training interpretable glassbox models and explaining blackbox machine learning systems.
A free, self-taught curriculum for learning bioinformatics using online courses from top universities.
A Python scikit for building and analyzing recommender systems that handle explicit rating data.
A practical guide for researchers on how to properly structure and share data with statisticians to ensure efficient analysis.
A collection of teaching materials, code, and data for data analysis and machine learning projects with accompanying blog posts.
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