Machine Learning Algorithms, Models, and Systems Integration
This course focuses on understanding software versus algorithms versus models. It reviews machine learning model training, and integration lifecycle. It also reviews provenance and tractability in machine learning models and best practices for machine learning model integration into business processes.
What you will learn:
- Understand software versus algorithms versus models
- Examine machine learning model training and integration lifecycle
- Review provenance and tractability in machine learning models
- Determine best practices for machine learning model integration into business processes
This Course is part of the following Course Program:
Machine Learning: Predictive Analysis for Business Decisions
Courses included in this program:
Who should attend: Computer engineers, business executives, industry executives, industry leaders, business leaders, technical managers, data scientists, and data engineers
Grant Scott is an Assistant Professor in the Center for Geospatial Intelligence (CGI) and the Electrical Engineering and Computer Science Department at the 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.
Publication Year: 2020