awesome-machine-learning/README.md

A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.

If you want to contribute to this list, send me a pull request or contact me [@josephmisiti](https://www.twitter.com/josephmisiti)

## Python

#### Natural Language Processing

* [NLTK](http://www.nltk.org/) - A leading platform for building Python programs to work with human language data.
* [Pattern](http://www.clips.ua.ac.be/pattern) - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
* [TextBlob](http://textblob.readthedocs.org/) - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
* [jieba](https://github.com/fxsjy/jieba#jieba-1) - Chinese Words Segementation Utilities.
* [SnowNLP](https://github.com/isnowfy/snownlp) - A library for processing Chinese text.
* [loso](https://github.com/victorlin/loso) - Another Chinese segmentation library.
* [genius](https://github.com/duanhongyi/genius) - A Chinese segment base on Conditional Random Field.

#### General-Purpose Machine Learning

* [scikit-learn](http://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [pattern](https://github.com/clips/pattern) - Web mining module for Python.
* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.
* [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano).
* [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python.
* [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans.
* [PyBrain](https://github.com/pybrain/pybrain) - Another Python Machine Learning Library.
* [Crab](https://github.com/muricoca/crab) - A flexible, fast recommender engine.
* [python-recsys](https://github.com/ocelma/python-recsys) - A Python library for implementing a Recommender System.
* [BayesPy](https://github.com/maxsklar/BayesPy)

#### Data Analysis / Data Visualization

* [SciPy](http://www.scipy.org/) - A Python-based ecosystem of open-source software for mathematics, science, and engineering.
* [NumPy](http://www.numpy.org/) - A fundamental package for scientific computing with Python.
* [Numba](http://numba.pydata.org/) - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
* [NetworkX](https://networkx.github.io/) - A high-productivity software for complex networks.
* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
* [Open Mining](https://github.com/avelino/mining) - Business Intelligence (BI) in Python (Pandas web interface)
* [PyMC](https://github.com/pymc-devs/pymc) - Markov Chain Monte Carlo sampling toolkit.
* [zipline](https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library.
* [PyDy](https://pydy.org/) - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
* [SymPy](https://github.com/sympy/sympy) - A Python library for symbolic mathematics.
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
* [astropy](http://www.astropy.org/) - A community Python library for Astronomy.
* [matplotlib](http://matplotlib.org/) - A Python 2D plotting library.
* [bokeh](https://github.com/ContinuumIO/bokeh) - Interactive Web Plotting for Python.
* [plotly](https://plot.ly/python) - Collaborative web plotting for Python and matplotlib.
* [vincent](https://github.com/wrobstory/vincent) - A Python to Vega translator.
* [d3py](https://github.com/mikedewar/d3py) - A plottling library for Python, based on [D3.js](http://d3js.org/).
* [ggplot](https://github.com/yhat/ggplot) - Same API as ggplot2 for R.
* [Kartograph.py](https://github.com/kartograph/kartograph.py) - Rendering beautiful SVG maps in Python.
* [pygal](http://pygal.org/) - A Python SVG Charts Creator.
* [pycascading](https://github.com/twitter/pycascading)

#### Misc Scripts / iPython Notebooks

* [pattern_classification](https://github.com/rasbt/pattern_classification)
* [thinking stats 2](https://github.com/Wavelets/ThinkStats2)
* [hyperopt](https://github.com/hyperopt/hyperopt-sklearn)
* [numpic](https://github.com/numenta/nupic)
* [2012-paper-diginorm](https://github.com/ged-lab/2012-paper-diginorm)
* [ipython-notebooks](https://github.com/ogrisel/notebooks)
* [decision-weights](https://github.com/CamDavidsonPilon/decision-weights)

## Ruby

#### Natural Language Processing

* [Treat](https://github.com/louismullie/treat) -  Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
* [Ruby Linguistics](http://www.deveiate.org/projects/Linguistics/) -  Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independant front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
* [Stemmer](https://github.com/aurelian/ruby-stemmer) - Expose libstemmer_c to Ruby 
* [Ruby Wordnet](http://www.deveiate.org/projects/Ruby-WordNet/) - This library is a Ruby interface to WordNet
* [Raspel](http://sourceforge.net/projects/raspell/) - raspell is an interface binding for ruby 
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer) - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing
* [Twitter-text-rb](https://github.com/twitter/twitter-text-rb) - A library that does auto linking and extraction of usernames, lists and hashtags in tweets

#### General-Purpose Machine Learning

* [Ruby Machine Learning](https://github.com/tsycho/ruby-machine-learning) - Some Machine Learning algorithms, implemented in Ruby
* [Machine Learning Ruby](https://github.com/mizoR/machine-learning-ruby)
* [jRuby Mahout](https://github.com/vasinov/jruby_mahout) - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby.
* [CardMagic-Classifier](https://github.com/cardmagic/classifier) - A general classifier module to allow Bayesian and other types of classifications.

#### Data Analysis / Data Visualization

* [rsruby](https://github.com/alexgutteridge/rsruby) - Ruby - R bridge
* [data-visualization-ruby](https://github.com/chrislo/data_visualisation_ruby) - Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby
* [ruby-plot](https://www.ruby-toolbox.com/projects/ruby-plot) - gnuplot wrapper for ruby, especially for plotting roc curves into svg files
* [plot-rb](https://github.com/zuhao/plotrb) - A plotting library in Ruby built on top of Vega and D3.
* [scruffy](http://www.rubyinside.com/scruffy-a-beautiful-graphing-toolkit-for-ruby-194.html) - A beautiful graphing toolkit for Ruby
* [SciRuby](http://sciruby.com/)
* [Glean](https://github.com/glean/glean) - A data management tool for humans
* [Bioruby](https://github.com/bioruby/bioruby)
* [Arel](https://github.com/nkallen/arel)

#### Misc

* [Big Data For Chimps](https://github.com/infochimps-labs/big_data_for_chimps)

## R

#### General-Purpose Machine Learning

* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [Machine Learning For Hackers](https://github.com/johnmyleswhite/ML_for_Hackers)

#### Data Analysis / Data Visualization

* [Learning Statistics Using R](http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/)

## Javascript

#### Natural Language Processing

* [Twitter-text-js](https://github.com/twitter/twitter-text-js)
* [NLP.js](https://github.com/nicktesla/nlpjs)


#### Data Analysis / Data Visualization

* [High Charts](http://www.highcharts.com/)
* [NVD3.js](http://nvd3.org/)
* [dc.js](http://dc-js.github.io/dc.js/)
* [chartjs](http://www.chartjs.org/)
* [dimple](http://dimplejs.org/)
* [amCharts](http://www.amcharts.com/)


#### General-Purpose Machine Learning

* [Convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/) [DEEP LEARNING]
* [Clustering.js](https://github.com/tixz/clustering.js)
* [Decision Trees](https://github.com/serendipious/nodejs-decision-tree-id3)
* [Node-fann](https://github.com/rlidwka/node-fann)
* [Kmeans.js](https://github.com/tixz/kmeans.js)
* [LDA.js](https://github.com/primaryobjects/lda)
* [Learning.js](https://github.com/yandongliu/learningjs)
* [Machine Learning](http://joonku.com/project/machine_learning)
* [Node-SVM](https://github.com/nicolaspanel/node-svm)
* [Brain](https://github.com/harthur/brain)


## Scala

#### Natural Language Processing

* [ScalaNLP](http://www.scalanlp.org/) - ScalaNLP is a suite of machine learning and numerical computing libraries.
* [Breeze](https://github.com/scalanlp/breeze) - Breeze is a numerical processing library for Scala.
* [Chalk](https://github.com/scalanlp/chalk) - Chalk is a natural language processing library.
* [FACTORIE](https://github.com/factorie/factorie) - FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.

#### Data Analysis / Data Visualization

* [Scalding](https://github.com/twitter/scalding)
* [Summing Bird](https://github.com/twitter/summingbird)
* [Algebird](https://github.com/twitter/algebird)
* [xerial](https://github.com/xerial/xerial)
* [simmer](https://github.com/avibryant/simmer)
* [PredictionIO](https://github.com/PredictionIO/PredictionIO)

#### General-Purpose Machine Learning

* [Conjecture](https://github.com/etsy/Conjecture)
* [brushfire](https://github.com/avibryant/brushfire)
* [ganitha](https://github.com/tresata/ganitha)
* [adam](https://github.com/bigdatagenomics/adam)
* [bioscala](https://github.com/bioscala/bioscala)

## Java

#### Natural Language Processing

* [CoreNLP] (http://nlp.stanford.edu/software/corenlp.shtml)
* [Stanford Parser] (http://nlp.stanford.edu/software/lex-parser.shtml)
* [Stanford POS Tagger] (http://nlp.stanford.edu/software/tagger.shtml)
* [Stanford Name Entity Recognizer] (http://nlp.stanford.edu/software/CRF-NER.shtml)
* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml)
* [Tregex, Tsurgeon and Semgrex](http://nlp.stanford.edu/software/tregex.shtml)
* [Stanford Phrasal: A Phrase-Based Translation System](http://nlp.stanford.edu/software/phrasal/)
* [Stanford English Tokenizer](http://nlp.stanford.edu/software/tokenizer.shtml)
* [Stanford Tokens Regex](http://nlp.stanford.edu/software/tokensregex.shtml)
* [Stanford Temporal Tagger](http://nlp.stanford.edu/software/sutime.shtml)
* [Stanford SPIED](http://nlp.stanford.edu/software/patternslearning.shtml)
* [Stanford Topic Modeling Toolbox](http://nlp.stanford.edu/software/tmt/tmt-0.4/)
* [Twitter Text Java](https://github.com/twitter/twitter-text-java)

#### General-Purpose Machine Learning

* [Mahout](https://github.com/apache/mahout)
* [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml)

#### Data Analysis / Data Visualization

* [Hadoop](https://github.com/apache/hadoop-mapreduce)
* [Spark](https://github.com/apache/spark)
* [Impala](https://github.com/cloudera/impala)

## Go

#### Natural Language Processing

* [go-porterstemmer](https://github.com/reiver/go-porterstemmer)
* [paicehusk](https://github.com/Rookii/paicehusk)
* [snowball](https://bitbucket.org/tebeka/snowball)

#### General-Purpose Machine Learning

* [Go Learn](https://github.com/sjwhitworth/golearn)
* [go-pr](https://github.com/daviddengcn/go-pr)
* [bayesian](https://github.com/jbrukh/bayesian)
* [go-galib](https://github.com/thoj/go-galib)

#### Data Analysis / Data Visualization

* [go-graph](https://github.com/StepLg/go-graph)
* [SVGo](http://www.svgopen.org/2011/papers/34-SVGo_a_Go_Library_for_SVG_generation/)

## Matlab

#### Natural Language Processing

* [NLP](https://amplab.cs.berkeley.edu/2012/05/05/an-nlp-library-for-matlab/)

#### General-Purpose Machine Learning

* [Training a deep autoencoder or a classifier 
on MNIST digits](http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) [DEEP LEARNING]
* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html)
* [Spider](http://people.kyb.tuebingen.mpg.de/spider/)
* [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab)
* [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download)

#### Data Analysis / Data Visualization

* [matlab_gbl](https://www.cs.purdue.edu/homes/dgleich/packages/matlab_bgl/)
* [gamic](http://www.mathworks.com/matlabcentral/fileexchange/24134-gaimc---graph-algorithms-in-matlab-code)

## Julia

#### General-Purpose Machine Learning

* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.
* [DA](https://github.com/trthatcher/DA.jl) - Julia package for Regularized Discriminant Analysis
* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression)
* [Local Regression](https://github.com/dcjones/Loess.jl) - Local regression, so smooooth!
* [Naive Bayes](https://github.com/nutsiepully/NaiveBayes.jl) - Simple Naive Bayes implementation in Julia
* [Mixed Models](https://github.com/dmbates/MixedModels.jl) - A Julia package for fitting (statistical) mixed-effects models
* [Simple MCMC](https://github.com/fredo-dedup/SimpleMCMC.jl) - basic mcmc sampler implemented in Julia
* [Distance](https://github.com/JuliaStats/Distance.jl) - Julia module for Distance evaluation
* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor
* [Neural](https://github.com/compressed/neural.jl) - A neural network in Julia
* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia
* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia
* [Online Learning](https://github.com/lendle/OnlineLearning.jl)
* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
* [Clustering](https://github.com/JuliaStats/Clustering.jl) - Basic functions for clustering data: k-means, dp-means, etc.
* [SVM](https://github.com/JuliaStats/SVM.jl) - SVM's for Julia
* [Kernal Density](https://github.com/JuliaStats/KernelDensity.jl) - Kernel density estimators for julia
* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction
* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization


#### Natural Language Processing

* [Topic Models](https://github.com/slycoder/TopicModels.jl) - TopicModels for Julia
* [Text Analysis](https://github.com/johnmyleswhite/TextAnalysis.jl) - Julia package for text analysis


#### Data Analysis / Data Visualization

* [Graph Layout](https://github.com/IainNZ/GraphLayout.jl) - Graph layout algorithms in pure Julia
* [Data Frames Meta](https://github.com/JuliaStats/DataFramesMeta.jl) - Metaprogramming tools for DataFrames
* [Julia Data](https://github.com/nfoti/JuliaData) - library for working with tabular data in Julia
* [Data Read](https://github.com/WizardMac/DataRead.jl) - Read files from Stata, SAS, and SPSS
* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia
* [Gladfly](https://github.com/dcjones/Gadfly.jl) - Crafty statistical graphics for Julia.
* [Stats](https://github.com/johnmyleswhite/stats.jl) - Statistical tests for Julia 

* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R
* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia
* [Distributions](https://github.com/JuliaStats/Distributions.jl) - A Julia package for probability distributions and associated functions.
* [Data Arrays](https://github.com/JuliaStats/DataArrays.jl) - Data structures that allow missing values
* [Time Series](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia
* [Sampling](https://github.com/JuliaStats/Sampling.jl) - Basic sampling algorithms for Julia

#### Misc Stuff / Presentations

* [JuliaCon Presentations](https://github.com/JuliaCon/presentations) - Presentations for JuliaCon
* [SignalProcessing](https://github.com/davidavdav/SignalProcessing) - Signal Processing tools for Julia
* [Images](https://github.com/timholy/Images.jl) - An image library for Julia

## Credits

* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
* The few go reference I found where pulled from [this page](https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning)