Machine Learning in a Data-Driven Business Environment
This course focuses on comprehending diverse enterprise data sources for machine learning. It discusses managing multi-facet enterprise data to enable machine learning, reviews exploratory analysis of multi-facet enterprise data, and training, validating, and testing machine learning models.
What you will learn:
- Comprehend diverse enterprise data sources for machine learning
- Manage multi-facet enterprise data to enable machine learning
- Analyze multi-facet enterprise data
- Review training, validating and testing machine learning models
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