Talk Machine learning with Scikits.learn

Presented by Fabian Pedregosa in Advanced tutorial track 2011 on 2011/08/26 from 11:00 to 12:30
Abstract

A guided tour through of some of the features in scikits.learn and how they are being used to solve real-world problems: from handwritten digits classification to stock market prediction.

Target audience : Engineers and scientists using Python for scientific and numerical computing. No knowledge needed in statistical learning.

Prerequisites: Being able to code scripts and function in Python. Basic knowledge of numpy and matplotlib.

Software requirements: IPython, scikits.learn, matplotlib.

Outline:

  • Datasets in scikit-learn.
  • Classification: Support Vector Machines and its variants. Example: recognizing handwritten digits.
  • Clustering: KMeans, Affinity Propagation. Example: finding structure in the stock market.
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