Here is a list of top Python Machine learning projects on GitHub. A continuously updated list of open source learning projects is available on Pansop.
scikit-learn is a Python module for machine learning built on top of SciPy.It features various classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Official source code repo:
The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns. NuPIC is suited to a variety of problems, particularly anomaly detection and prediction of streaming data sources.
Pattern is a web mining module for Python. It has tools for Data Mining, Natural Language Processing, Network Analysis and Machine Learning. It supports vector space model, clustering, classification using KNN, SVM, Perceptron
Pylearn2 is a library designed to make machine learning research easy. Its a library based on Theano
Ramp is a python library for rapid prototyping of machine learning solutions. It's a light-weight pandas-based machine learning framework pluggable with existing python machine learning and statistics tools (scikit-learn, rpy2, etc.). Ramp provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.
Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs, k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.For unsupervised learning, milk supports k-means clustering and affinity propagation.
Skdata is a library of data sets for machine learning and statistics. This module provides standardized Python access to toy problems as well as popular computer vision and natural language processing data sets.
It's a library consisting of useful tools and extensions for day-to-day data science tasks.
A collection of sample applications built using Amazon Machine Learning.
REP is environment for conducting data-driven research in a consistent and reproducible way. It has a unified classifiers wrapper for variety of implementations like TMVA, Sklearn, XGBoost, uBoost. It can train classifiers parallely on a cluster. It support of interactive plots