9. Machine learning

After preprocessing (f)MRI data,  we can do more exploration for these data and mining more medical information. Machine learning is one of the perfect examples to explain it. Here I will introduce some libraries with Python to do machine learning for neuroimaging.

9.1 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 modelling, classification, decoding, or connectivity analysis.
Among the best libraries of machine learning for neuroimaging with Python, I think Nilearn is the best choice for us. In the next part, I will show you some simple examples to help you quickly get into Nilearn. Nilearn also supplies interface that helps you to automatically download data from OpenfMRI and NeuroVault ( a new home for all brain statistical maps).