A required course in the Predictive Analytics Certificate Program.
Learn how to use the basics of predictive analytics and modeling data to determine which algorithms to use. Understand the similarities and differences and which options affect the models most. Discover how to verify and validate your model. Topics covered include predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Gain a deeper understanding of how algorithms work qualitatively by reviewing best practices and the influence of various options on predictive models.
Prerequisites: I&C SCI X425.61 Introduction to Predictive Analytics and I&C SCI X425.63 Effective Data Preparation. See enrollment confirmation for login information.
William J. Henry, M.S., is a scientific programmer at the Navy Research Laboratory in Monterey where he regularly develops data based applications in Python. Previously, at EarthRisk Technologies, he led the development of a neural network ensemble temperature forecast model.
Textbooks for your course may be purchased from any vendor or bookseller of your choice.
No textbooks are required for this course.
|Event||Date||Day||Start Time||End Time||Location||Room
|START||06/25/2018||Monday||---||---||Online (Access Begins)||---
|END||08/12/2018||Sunday||---||---||Online (Access Ends)||---