scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation

Microsoft ML for Apache Spark -> A distributed machine learning framework Apache Spark

Shapley -> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.

igel -> A delightful machine learning tool that allows you to train/fit, test and use models

ML Model building -> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.

ML/DL project template

PyTorch Geometric Temporal -> A temporal extension of PyTorch Geometric for dynamic graph representation learning.

Little Ball of Fur -> A graph sampling extension library for NetworkX with a Scikit-Learn like API.

Karate Club -> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.

Auto -> Automatically Build Variant Interpretable ML models fast! Auto

PyOD -> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.

steppy -> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.

steppy-toolkit -> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.

CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found

Couler - Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

auto_ml - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.

machine learning - automated build consisting of a

XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library.

Apache SINGA · Distributed deep learning system

Distributed deep learning system

Bayesian Methods for Hackers - Book/iPython notebooks on Probabilistic Programming in Python.

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

MLlib: RDD-based API - Spark 3.2.0 Documentation

Hydrosphere Mist - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.

metric-learn - A Python module for metric learning.

Intel(R) Extension for Scikit-learn - A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.

SimpleAI Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.

AstroML: Machine Learning and Data Mining for Astronomy — astroML 1.0 documentation

graphlab-create - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.

BigML.com

Machine learning made beautifully simple for everyone take your business to the next level with the leading machine learning platform

pattern - Web mining module for Python.

NuPIC - Numenta Platform for Intelligent Computing.

Pylearn2 - A Machine Learning library based on

keras - High-level neural networks frontend for

Lasagne - Lightweight library to build and train neural networks in Theano.

hebel - GPU-Accelerated Deep Learning Library in Python.

Chainer - Flexible neural network framework.

Prophet

Prophet is a forecasting procedure implemented in r and python it is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts