Machine Learning: Predictive Analysis for Business Decisions

The course series covers machine learning and its surrounding aspects, including models, algorithms, and platforms to improve decision making.

0.5 CEU / 5 PDH credits
Launched 2020
5 courses
5 hours

Course Description

This course series provides an overview of machine learning in the age of big data, cloud computing, and our data-saturated society. Business leaders will learn of the various types of machine learning, and how they can be used with a variety of enterprise data holdings and publicly available data to develop deeper insights in the business environment; understand basic computational intelligence paradigms, and how machine learning is integral to the field; learn the technical vocabulary and high-level concepts of machine learning in a manner that demystifies the topic and enables them to ask the right questions on the deployment of machine learning into business operations.

Course Objectives

Examine the fundamental types of machine learning that drive business insights
Explore how to manage multi-facet enterprise data to enable machine learning
Review the application of data mining and diagnostic analytics to measure business performance
Gain an understanding of software, algorithms, and models
Understand the concepts and techniques necessary for deploying scalable machine learning into business processes

Authors and Instructors

Grant Scott

Assistant Professor in the Center for Geospatial Intelligence (CGI) and the Electrical Engineering and Computer Science Department, University of Missouri

Throughout his career, Dr. Grant Scott has conducted extensive research on scaling machine learning up for big data. His research focuses on Applied Machine Learning, Computer Vision, as well as Advanced Pattern Analysis, High-dimensional Data Analytics, Advanced Data Systems, and Multi-modal Analytics.

Dr. Scott is a Senior Member of the IEEE Computational Intelligence Society and the IEEE Geoscience Remote Sensing Society.