Thanks for the summary. I was there as well, and it seemed like scikit-learn had a strong showing. It seemed as though many talks that weren't directly on scikit-learn still mentioned it or used the models during the presentation. On Fri, Jun 30, 2017 at 9:47 AM, Francois Dion <francois.dion@gmail.com> wrote:

This past weekend was the Numfocus sponsored Data Intelligence conference at Capital One, in Mclean, Virginia (close to Washington DC for those not familiar with the US geography).

A few presentations mentioned/used scikit-learn, including Ben Bengfort's Visual Pipelines ( http://data-intelligence.ai/presentations/13 ), Zachary Beaver's Airflow + Scikit-Learn ( http://data-intelligence.ai/presentations/19 ) and Pramit Choudary's Learning to Learn Model Behavior ( http://data-intelligence.ai/presentations/22 ), to name a few.

I presented "Seeking Exotics" on Sunday (http://data-intelligence.ai/ presentations/21), on anomalous and erroneous data, and how statistics, visualizations and scikit-learn can help (covered PCA, truncatedSVD, t-sne, ellipticenvelope, one class classifiers and scikit-learn related imbalanced-learn and sk-sos).

One of the slide I had up resonated quite a bit with the audience, both in person and on social media:

https://twitter.com/tnfilipiak/status/878999245076008960

The notebooks are on github: https://github.com/fdion/seeking_exotics

Francois -- @f_dion - https://about.me/francois.dion - https://www.linkedin.com/in/ francois-dion-8b639b79/

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