We may earn an affiliate commission when you visit our partners.
Jon Reifschneider

Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.

Read more

Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.

This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems. Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards. The courses in this Specialization focus on the intuition behind these technologies, with no programming required, and merge theory with practical information including best practices from industry. Professionals and aspiring professionals from a diverse range of industries and functions, including product managers and product owners, engineering team leaders, executives, analysts and others will find this program valuable.

Enroll now

Share

Help others find Specialization from Coursera by sharing it with your friends and followers:

What's inside

Three courses

Machine Learning Foundations for Product Managers

(24 hours)
In this course, you will gain a foundational understanding of machine learning, including its types, challenges, algorithms, and evaluation techniques. You will also learn about deep learning and its strengths and challenges relative to other forms of machine learning.

Managing Machine Learning Projects

This course focuses on managing machine learning projects. It covers identifying opportunities for ML, applying the data science process, evaluating technology decisions, and leading ML projects from ideation through production.

Human Factors in AI

(16 hours)
This final course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the critical human factors in developing AI-based products. Participants will learn about the role of data privacy in AI systems, the challenges of designing ethical AI, and approaches to identify sources of bias and mitigate fairness issues.

Learning objectives

  • Identify when and how machine learning can applied to solve problems
  • Apply human-centered design practices to design ai product experiences that protect privacy and meet ethical standards
  • Lead machine learning projects using the data science process and best practices from industry

Save this collection

Save AI Product Management to your list so you can find it easily later:
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser