I found recently an oDesk job which can match my interest. Client says: “I would like that predictor to be written in Python only, and leverage only publicly-available libraries (mlpy, scipy,scikit etc.)“. Well, it would be good idea to utilize more than one package and check for output F-score, so I googled the most known machine learning packages for Python, there they are:
- MLPY (https://mlpy.fbk.eu/)
- PyML (http://pyml.sourceforge.net/)
- Milk (http://pypi.python.org/pypi/milk/)
- Shogun (http://www.fml.tuebingen.mpg.de/… Code is in C++ but it has a python wrapper.
- MDP (http://mdp-toolkit.sourceforge.n… Python library for data mining
- PyBrain (http://pybrain.org/)
- Orange (http://www.ailab.si/orange/): Statistical computing and data mining
- PYMVPA (http://www.pymvpa.org/)
- scikit-learn (http://scikit-learn.org): Numpy / Scipy / Cython implementations for major algorithms + efficient C/C++ wrappers
- Monte (http://montepython.sourceforge.n… a software for gradient-based learning in Python
- Rpy2 (http://rpy.sourceforge.net/): Python wrapper for R
MLPY and PyML seem to be the most known and mainstream choices. Regarding the list above – Anaconda Python distribution seems to include only scikit-learn package. On the other hand, if your task is connected with NLP only, NLTK package may be enough.