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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