Awesome Machine Learning

Image processing in Python

An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more

A Python wrapper for Google Tesseract

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

A scikit-learn compatible neural network library that wraps PyTorch

⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.

A library of extension and helper modules for Python's data analysis and machine learning libraries.

An Engine-Agnostic Deep Learning Framework in Java

Fast data visualization and GUI tools for scientific / engineering applications

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Main repository for Vispy

Fast, flexible and easy to use probabilistic modelling in Python.

🛠 All-in-one web-based IDE specialized for machine learning and data science.

Algorithmic Trading in Python with Machine Learning

Accelerated deep learning R&D

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

NumPy and Pandas interface to Big Data

a delightful machine learning tool that allows you to train, test, and use models without writing code

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

Shōgun

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

StellarGraph - Machine Learning on Graphs

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

Build, run and scale AI agents like API and microservices

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

An intuitive library to add plotting functionality to scikit-learn objects.

A modular active learning framework for Python

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Feature engineering and selection open-source Python library compatible with sklearn.

A toolkit for reproducible reinforcement learning research.

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Machine learning with dataframes

Hyper-parameter optimization for sklearn

ThunderSVM: A Fast SVM Library on GPUs and CPUs

The Python ensemble sampling toolkit for affine-invariant MCMC

Spearmint Bayesian optimization codebase

A security scanner for your LLM agentic workflows

Package geom implements efficient geometry types for geospatial applications.

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

ML powered analytics engine for outlier detection and root cause analysis.

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Machine Learning toolbox for Humans

No description.

Python package for Bayesian Machine Learning with scikit-learn API

A set of tools for creating and testing machine learning features, with a scikit-learn compatible API