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Machine Learning with Scikit-Learn

Translations: en

Description

PyData Amsterdam 2016

Description

Scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry. scikit-learn provides easy-to-use interfaces to perform advances analysis and build powerful predictive models. The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation and model selection.

Abstract

Scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry. scikit-learn provides easy-to-use interfaces to perform advances analysis and build powerful predictive models. The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline. We will also cover how to build machine learning models on text data, and how to handle very large datasets.

If you want to follow along, there will be material online that you can download beforehand: https://github.com/amueller/pydata-amsterdam-2016.

I usually recommend to install the anaconda python distribution before coming: https://store.continuum.io/cshop/anaconda/ and make sure that you are able to run the ipython notebook.

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