Machine Learning: Predictive Analysis for Business Decisions Course Program

Get machine learning technology training from IEEE. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. These courses are designed to help engineers stay up-to-date on the latest technologies. Produced and vetted by leading experts, you can count on IEEE Continuing and Professional Education courses to equip you with the information you need to stay current in your field.

Machine learning is a vital aspect of artificial intelligence (AI). Machine learning allows an AI system to learn from its experiences without it needing explicit programming. When applied in a business setting, this type of data-driven decision making becomes more accurate and accessible.

This five-course machine learning training program covers the following topics:

• Machine Learning in the Age of Enterprise Big Data
• Machine Learning in a Data-Driven Business Environment
• Sound Business Practices for Data Mining and Predictive Analysis
• Machine Learning Algorithms, Models, and Systems Integration
• Machine Learning Platforms, Technology, and Tools

Upon successful completion of Machine Learning: Predictive Analysis for Business Decisions course program, your engineers will receive valuable CEUs/PDHs from IEEE that can be used to maintain engineering licenses.

 

 

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The Machine Learning: Predictive Analysis for Business Decisions online training program includes the following courses:

Machine Learning in the Age of Enterprise Big Data
Examine the fundamental types of machine learning that drive business insights and review advanced computational intelligence for business processes.

Machine Learning in a Data-Driven Business Environment
Learn how to manage multi-facet enterprise data to enable machine learning and explore an exploratory analysis of multi-facet enterprise data, and training, validating, and testing machine learning models.

Sound Business Practices for Data Mining and Predictive Analysis
Learn about the application of data mining and diagnostic analytics to measure business performance and how they build upon business performance measurements to achieve advanced insights with predictive and perspective analytics.

Machine Learning Algorithms, Models, and Systems Integration
Understand software, algorithms, and models, and examine provenance and tractability in machine learning models and best practices for machine learning model integration into business processes.

Machine Learning Platforms, Technology, and Tools
Explore big data lakes and data warehouses, and discuss these two alternative enterprise repositories, and their relative strengths and drawbacks. Understand the concepts and techniques necessary for deploying scalable machine learning into business processes.

 

 

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IEEE has partnered with a top expert in machine learning, electrical engineering, and computer science.

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.