Predictive Analytics from the ground up with real world use cases.

Charles Wheelus will present his work in machine learning, complete with real-world case studies from business and academia. This presentation is intended for anyone interested in machine learning; whether you are a novice or an experienced practitioner, there should be something for everybody. Discussion topics include: - How computers and humans differ in pattern recognition - Components of a successful data science project - Right data for the job: training data, attribute data - Data preparation - Linear and logistic regression - Classification / numeric prediction - Supervised / unsupervised learning - Recent use cases - Building a predictive analytic model using WEKA (live example) - Evaluating the efficacy of your models - Make your models “better" - Data science resources - Conclusion and questions

Charles Wheelus is the Principal Data Scientist at Cequint, in Fort Lauderdale, where he specializes in performance analytics for wireless networks. He is also the inventor of the technology behind Candidate.Guru, one of South Florida’s hottest machine learning startups. In addition to his professional duties, Mr. Wheelus is a mentor at FAU’s Tech Runway and is a Ph.D. candidate in the Computer Science department at FAU. His research interests include: data mining, security analytics, machine learning, and predictive analytics. His research domain for dissertation is predictive analytics for cyber and network security.