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A collection of  Python Machine learning open source projects.

This is a part of Python Knowledge and Resources List

  1. scikit-learn

    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
  2. NuPIC

    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.

    https://github.com/numenta/nupic


  3. Pattern

    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

    https://github.com/clips/pattern


  4. Pylearn2

    Pylearn2 is a library designed to make machine learning research easy. Its a library based on Theano

    https://github.com/lisa-lab/pylearn2

  5. Ramp

    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.

    https://github.com/kvh/ramp


  6. MILK

    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

    https://github.com/luispedro/milk


  7. skdata

    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.

    http://jaberg.github.com/skdata

  8. mlxtend

    Its a library consisting of useful tools and extensions for the day-to-day data science tasks.

    https://github.com/rasbt/mlxtend
  9. Python Machine Learning Samples

    A collection of sample applications built using Amazon Machine Learning.

    https://github.com/awslabs/machine-learning-samples


  10. REP

    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

    https://github.com/yandex/rep


  11. Python-ELM

    This is an implementation of the Extreme Learning Machine in Python, based on scikit-learn.

  12. fuel
    Fuel provides your machine learning models with the data they need to learn. it has interfaces to common datasets such as MNIST, CIFAR-10 (image datasets), Google's One Billion Words (text). It gives you the ability to iterate over your data in a variety of ways, such as in minibatches with shuffled/sequential examples
    https://github.com/mila-udem/fuel
  13. nilearni
    Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data.
    It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
  14. Bob

    Bob is a free signal-processing and machine learning toolbox
    The toolbox is written in a mix of Python and C++ and is designed to be both efficient and reduce development time. It is composed of a reasonably large number of packages that implement tools for image, audio & video processing, machine learning and pattern recognition.

  15. 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.

    Website: http://scikit-learn.org/stable/

  16. nolearn

    This package contains a number of utility modules that are helpful with machine learning tasks. Most of the modules work together with scikit-learn, others are more generally useful.

  17. PyBrain
    PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library
    Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
  18. IEPY

    IEPY is an open source tool for Information Extraction focused on Relation Extraction

    It's aimed at:

    • users needing to perform Information Extraction on a large dataset.
    • scientists wanting to experiment with new IE algorithms.
    https://github.com/machinalis/iepy
  19. Quepy

    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.

    Currently Quepy provides support for Sparql and MQL query languages. We plan to extended it to other database query languages

    https://github.com/machinalis/quepy
  20. Feature Forge

    A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
    This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, etc.), and particularly helpful if you use scikit-learn (although this can work if you have a different algorithm).

    https://github.com/machinalis/featureforge
  21. Hebel

    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.

    https://github.com/hannes-brt/hebel

  22. Optunity

    Optunity is a free software package dedicated to hyperparameter optimization to automatically find suitable hyperparameters for a given learning task. Optunity's dedicated optimizers are a drop-in replacement for grid search, which will yield better hyperparameters while requiring less computation time. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in popular machine learning environments, such as scikit-learn, OpenCV and Spark's MLlib.

  23. Weather
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