Data Science
Showing 36 of 506 projects
A cross-platform desktop application for JupyterLab, providing the easiest way to run Jupyter notebooks locally.
A comprehensive collection of tutorials, examples, and resources for understanding and solving machine learning and pattern classification problems.
An open-source CLI tool for implementing CI/CD workflows with a focus on MLOps, automating ML experiments and reporting.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
A Python library that simplifies data integration between pandas and AWS services like Athena, S3, Redshift, and more.
A Python library that simplifies data integration between pandas and AWS services like Athena, S3, Redshift, and more.
A curated list of resources for constructing, analyzing, and visualizing network data across various disciplines.
A curated list of practical resources for responsible machine learning, covering interpretability, governance, safety, and ethics.
An open-source solution for continuous validation of machine learning models and data, from research to production.
Run code interactively, inspect data, and plot using Jupyter kernels directly inside the Atom text editor.
A collection of Jupyter notebooks accompanying a 10-part video series teaching machine learning with Python's scikit-learn library.
A Java dataframe and visualization library for data loading, cleaning, transformation, and analysis.
A Jupyter/IPython extension that transforms notebooks into interactive Reveal.js slideshows with live execution.
R code examples from the 'Machine Learning for Hackers' book, demonstrating practical machine learning techniques.
A Python package for interactive mapping and geospatial analysis with minimal coding in Jupyter notebooks.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A Python implementation of the grammar of graphics for creating statistical visualizations.
A Python library that simplifies chart creation for data scientists with consistent data formats and smart defaults.
A Python library that simplifies chart creation for data scientists with consistent data formats and smart defaults.
The fastest way to build data pipelines with iterative development and deployment anywhere.
An easy-to-use blogging platform with enhanced support for Jupyter Notebooks, Word docs, and Markdown, powered by GitHub Actions.
A web-based IDE for machine learning and data science with pre-installed libraries and tools, deployable via Docker.
A debugging and visualization tool for data science, deep learning, and reinforcement learning in Jupyter Notebook.
A curated list of Python software for data science, covering machine learning, deep learning, visualization, and data manipulation.
A comprehensive collection of machine learning tutorials and implementations in Python, covering algorithms from scratch to production deployment.
Koalas provides the pandas DataFrame API on Apache Spark, enabling data scientists to work with big data using familiar pandas syntax.
A Jupyter kernel for C++ that enables interactive computing with cling interpreter and xeus protocol.
A Python toolkit for causal and probabilistic reasoning using graphical models like Bayesian Networks and Structural Equation Models.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
A visual roadmap and keyword mind map for students learning Natural Language Processing, from basics to SOTA models.
A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.
An engine and API for running .NET and other languages interactively in notebooks, REPLs, and embedded coding experiences.
A Python machine learning toolkit for time series analysis with scikit-learn compatible API.
A Python library for automated exploratory data analysis (EDA) with high-density visualizations and target analysis in two lines of code.
A high-performance, easy-to-use, and scalable machine learning package for linear models, factorization machines, and field-aware factorization machines.
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