Scikit Learn Workshop with Andreas Mueller 3-30-2015

Scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners.

This talk will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines, model evaluation, parameter search, and out-of-core learning.

Apart from metrics for model evaluation, we will cover how to evaluate model complexity, and how to tune parameters with grid search, randomized parameter search, and what their trade-offs are. We will also cover out of core text feature processing via feature hashing.

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Andreas is an Assistant Research Scientist at the NYU Center for Data Science, building a group to work on open source software for data science. Previously he worked as a Machine Learning Scientist at Amazon, working on computer vision and forecasting problems. He is one of the core developers of the scikit-learn machine learning library, and maintained it for several years.

Тэги:

#Scikit-learn #Programming_Language_(Software_Genre) #Meetup_(Website) #Data_Science #Analytics_(Industry) #Python_(Programming_Language)

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