josephmisiti/awesome-machine-learning
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* [Bolt](https://github.com/pprett/bolt) - Bolt Online Learning Toolbox
* [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
* [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python
* [neuropredict](https://github.com/raamana/neuropredict) - Easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and other types of features
* [neuropredict](https://github.com/raamana/neuropredict) - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful.
* [imbalanced-learn](http://contrib.scikit-learn.org/imbalanced-learn/) - Python module to perform under sampling and over sampling with various techniques.
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
* [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework.