Room 110, Frank and Estella Beam Hall, the building next to the Students Union
Google Maps Link: https://www.google.com/maps/place/Frank+and+Estella+Beam+Hall,+Las+Vegas,+NVfirstname.lastname@example.org,-115.1398163,15z/data=!4m2!3m1!1s0x80c8c500c014908f:0x7cfa05fb8e6e9a2f
6:30pm - 7:00pm: - Pizza, Drinks, Socialize
7:00pm - 8:00pm: - Presentation and Demo
8:00pm - 8:15pm - Questions and Closing Remarks
Machine Learning has gone mainstream and now belongs in the toolkit of the modern analyst. This meetup will help create a foundation for analysts who might be new to MACHINE LEARNING concepts as well as provide context to more machine learning-seasoned analysts who want a crosswalk from traditional statistics to machine learning. We will discuss the basics of machine learning from the perspective of someone trained in statistics and other traditional academic disciplines which use statistical computing. We will cover the “typical” methods used by someone looking to use machine learning methods in the context of solving business problems such as predicting customer churn, evaluating upsell/cross-sell opportunities or segmenting customers.
The meetup will step through several examples using R and other open-source tools and emphasize the choice of method for each business problem. We hope all levels of data scientist will join us to learn more about using these exciting new technologies to solve real business problems.
Ken is an Analytics Architect and Evangelist at H2O (http://www.h2o.ai/). Ken is a reformed academic economist who likes to empower customers to solve problems with data. Ken’s primary passion is teaching and explaining. He likes to simplify and tell stories.
Ken has spent time in academia (Middle Tennessee State University, U of Cincinnati, Peace College) consulting (Deloitte) and software development (SAS). He has a Ph.D. in Economics from the University of Kentucky in Lexington and his work on price optimization has been published in peer-reviewed journals.