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A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.
updates
2014-07-15 19:54:28 +00:00
If you want to contribute to this list, send me a pull request or contact me [@josephmisiti](https://www.twitter.com/josephmisiti)
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## Python
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#### Natural Language Processing
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* [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.
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#### 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.
#### 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.
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#### 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)
* []
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## Ruby
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#### 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/) - NLTK for Ruby
* [Stemmer](https://github.com/aurelian/ruby-stemmer)
* [Ruby Wordnet](http://www.deveiate.org/projects/Ruby-WordNet/)
* [Raspel](http://sourceforge.net/projects/raspell/)
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer)
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#### General-Purpose Machine Learning
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* [Ruby Machine Learning](https://github.com/tsycho/ruby-machine-learning)
* [Machine Learning Ruby](https://github.com/mizoR/machine-learning-ruby)
* [jRuby Mahout](https://github.com/vasinov/jruby_mahout)
* [CardMagic-Classifier](https://github.com/cardmagic/classifier)
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#### Data Analysis / Data Visualization
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* [rsruby](https://github.com/alexgutteridge/rsruby)
* [data-visualization-ruby](https://github.com/chrislo/data_visualisation_ruby)
* [ruby-plot](https://www.ruby-toolbox.com/projects/ruby-plot)
* [plot-rb](https://github.com/zuhao/plotrb)
* [scruffy](http://www.rubyinside.com/scruffy-a-beautiful-graphing-toolkit-for-ruby-194.html)
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## Scala
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#### Natural Language Processing
* TODO
#### Data Analysis / Data Visualization
* TODO
#### General-Purpose Machine Learning
* [Conjecture](https://github.com/etsy/Conjecture)
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## Java
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#### 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/)
#### 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)
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## Go
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#### Natural Language Processing
* TODO
#### General-Purpose Machine Learning
* [Go Learn](https://github.com/sjwhitworth/golearn)
#### Data Analysis / Data Visualization
* TODO
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## R
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#### Natural Language Processing
* TODO
#### General-Purpose Machine Learning
* TODO
#### Data Analysis / Data Visualization
* TODO
## Matlab
#### Natural Language Processing
* TODO
#### General-Purpose Machine Learning
* TODO
#### Data Analysis / Data Visualization
* TODO
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## Julia
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#### Natural Language Processing
* TODO
#### General-Purpose Machine Learning
* TODO
#### Data Analysis / Data Visualization
* TODO
#### Misc Scripts + Presentations
* [JuliaCon Presentations](https://github.com/JuliaCon/presentations)
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credits
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## Credits
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credits
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* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
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