Wednesday 1:20 p.m.–4:40 p.m.

Diving deeper into Machine Learning with Scikit-learn

Olivier Grisel, Jake Vanderplas

Audience level:


This tutorial session is an hands-on workshop on applied Machine Learning with the scikit-learn library. We will dive deeper into scikit-learn model evaluation and automated parameter tuning. We will also study how to scale text classification models for sentiment analysis or spam detection and use IPython.parallel to leverage multi-CPU or ad-hoc cloud clusters.


Machine learning is the branch of computer science concerned with the development of algorithms to which can learn from previously-seen data in order to make predictions about future data. It has become an important aspect of work in a variety of applications: from optimization of web searches, to financial forecasts, to studies of the nature of the Universe. This tutorial will give an hands-on experience on some more advanced aspects of applied machine learning with the scikit-learn package. In particular we will focus on text processing, out-of-core learning and distributed model fitting, selection and ensembling.

Student Handout

No handouts have been provided yet for this tutorial