Data science

Top Machine Learning Projects that Can be Done Using Python

Top machines learning projects for you to try on
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

 

Scikit-learn
18845 commits, 404 contributors

www.github.com/scikit-learn/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.

 

Pylearn2
7027 commits, 117 contributors

 www.github.com/lisa-lab/pylearn2

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

 

NuPIC
4392 commits, 60 contributors

www.github.com/numenta/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.

 

Nilearn
2742 commits, 28 contributors

www.github.com/nilearn/nilearn

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 modeling, classification, decoding, or connectivity analysis.

 

PyBrain
969 commits, 27 contributors

www.github.com/pybrain/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.

 

Pattern
943 commits, 20 contributors

www.github.com/clips/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

 

Fuel
497 commits, 12 contributors

www.github.com/mila-udem/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 mini-batches with shuffled/sequential examples.

 

Bob
5080 commits, 11 contributors

www.github.com/idiap/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

 

Skdata
441 commits, 10 contributors

www.github.com/jaberg/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.

 

MILK
687 commits, 9 contributors

 www.github.com/luispedro/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.

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