Scikit-Learn for Easy Machine Learning: the Vision, the Tool, and the Project
Scikit-learn is a popular machine learning tool. What can it do for you?Why you you want to use it? What can you do with it? Where is it going?In this talk, I will discuss why and how scikit-learn became popular. Iwill argue that it is successful because of its vision: it fills an important slot in the rich ecosystem of data science. I will demonstrate how scikit-learn makes predictive analysis easy and yet versatile.I will shed some light on our development process: how do we, as a community, ensure the quality and the growth of scikit-learn? What are the new exciting development? What may we expect in the near future?
Gaël Varoquaux is an INRIA faculty researcher working on data science for brain imaging in the Neurospin brain research institute (Paris, France). His research focuses on modeling and mining brain activity in relation to cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.