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A collection of Python Machine learning open source projects.
This is a part of Python Knowledge and Resources List
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: https://github.com/scikit-learn/scikit-learn
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.
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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. r
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.
Its a library consisting of useful tools and extensions for the day-to-day data science tasks.https://github.com/rasbt/mlxtend
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
This is an implementation of the Extreme Learning Machine in Python, based on scikit-learn.
scikit-learn is an open source library for the Python. 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.
IEPY is an open source tool for Information Extraction focused on Relation Extraction
It's aimed at:
Quepy is a python framework to transform natural language questions to queries in a database query language. It can be easily customized to different kinds of questions in natural language and database queries. So, with little coding you can build your own system for natural language access to your database.https://github.com/machinalis/quepy
GPU-Accelerated Deep Learning Library in Python
Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.