Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY
Andreas Mueller

Andreas Mueller
Lecturer in Data Science, Columbia University

Website

Andreas Mueller is a research engineer at the NYU Center for Data Science, building open source software for data science. Previously, he worked as a machine-learning scientist at Amazon, developing solutions for computer vision and forecasting problems. Andreas is one of the core developers of the scikit-learn machine-learning library and has comaintained it for several years. His mission is to create open tools to lower the barrier of entry for machine-learning applications, promote reproducible science, and democratize the access to high-quality machine-learning algorithms.

Sessions

9:00am–12:30pm Tuesday, 09/27/2016
Data science & advanced analytics
Location: 3D 12 Level: Intermediate
Tags: pydata
Andreas Mueller (Columbia University)
Average rating: ****.
(4.00, 6 ratings)
Scikit-learn, which provides easy-to-use interfaces to perform advances analysis and build powerful predictive models, has emerged as one of the most popular open source machine-learning toolkits. Using scikit-learn and Python as examples, Andreas Mueller offers an overview of basic concepts of machine learning, such as supervised and unsupervised learning, cross-validation, and model selection. Read more.
3:45pm–4:25pm Wednesday, 09/28/2016
Location: O'Reilly Booth (Table B)
Andreas Mueller (Columbia University)
Join Andreas to discuss machine learning, data science—in particular with Python—and using scikit-learn. Read more.