Скачать или смотреть ODSC West 2015 | Andreas Mueller - "Machine Learning With scikit learn"

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 advanced analysis and build powerful predictive models.This workshop will provide an interactive introduction into using scikit-learn. on us 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. Please download the material beforehand. It will be provided at https://github.com/amueller/odscon-sf... It is recommended to use the anaconda python distribution, which comes with all packages that are needed for the workshop https://store.continuum.io/cshop/anac... The requirements are numpy, scipy, matplotlib, Jupyter notebook and scikit-learn. Make sure you can run the jupyter notebooks included in the material.

Bio: Andreas Mueller received his MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, he finalized his PhD thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, he joined the Center for Data Science at the New York University in the end of 2014. In his current position as assistant research engineer at the Center for Data Science, he works on open source tools for machine learning and data science. He is one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and has authored and contributed to a number of open source projects related to machine learning.