This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
This course is part of the AI Product Management Specialization
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About this Course
No prior experience in machine learning or programming required.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Modeling
- Project Management
- Machine Learning
No prior experience in machine learning or programming required.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Identifying Opportunities for Machine Learning
Organizing ML Projects
Data Considerations
ML System Design & Technology Selection
Reviews
- 5 stars86.53%
- 4 stars5.76%
- 3 stars3.84%
- 2 stars1.92%
- 1 star1.92%
TOP REVIEWS FROM MANAGING MACHINE LEARNING PROJECTS
Very important course for anyone interested in understanding the process involved in managing AI projects. Strongly recommended.
worth your time if you are a product manager, product owner or project manager that is interested in implementing ML
Excellent course! And the professor is a SME in the ML field. Looking forward to the next course.
About the AI Product Management Specialization

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