Machine Learning: Predictive Analysis for Business Decisions

  • Course Program

This course 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.

What you will learn:

  • 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

Courses included in this program:

Course Program Length: 5 hours

Who Should Attend: Computer engineers, business executives, industry executives,  industry leaders, business leaders,  technical managers, data scientists, and data engineers


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

Publication Year: 2020

ISBN: 978-1-7281-7749-6

Upon successful completion of this course program, you are eligible to receive a digital badge that can be shared on LinkedIn and other social networks.   If you would like to request a badge for this program, please contact us:

Machine Learning: Predictive Analysis for Business Decisions
  • Course Provider: Educational Activities
  • Course Number: EDP582CP
  • Credits: 0.5 CEU/ 5 PDH