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.

In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied.  To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology.  This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms.  The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.

At the conclusion of this course, you should be able to:
1) Explain how machine learning works and the types of machine learning
2) Describe the challenges of modeling and strategies to overcome them
3) Identify the primary algorithms used for common ML tasks and their use cases
4) Explain deep learning and its strengths and challenges relative to other forms of machine learning
5) Implement best practices in evaluating and interpreting ML models

Machine Learning Foundations for Product Managers

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.

At the conclusion of this course, you should be able to:
1) Identify opportunities to apply ML to solve problems for users
2) Apply the data science process to organize ML projects
3) Evaluate the key technology decisions to make in ML system design
4) Lead ML projects from ideation through production using best practices

Managing Machine Learning Projects

This third and 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.  The course begins with an introduction to human-centered design and the unique elements of user experience design for AI products.  Participants will then 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.  The course concludes with a comparison of human intelligence and artificial intelligence, and a discussion of the ways that AI can be used to both automate as well as assist human decision-making.

At the conclusion of this course, you should be able to:
1) Identify and mitigate privacy and ethical risks in AI projects
2) Apply human-centered design practices to design successful AI product experiences
3) Build AI systems that augment human intelligence and inspire model trust in users

AI Product Management Specialization

About the Program

Description

Deliverables

0 Credits

Academic Excellence

Outcomes

Autodesk Certified User: AutoCAD$281.25

Autodesk Certified User: Inventor for Mechanical Design$281.25

Autodesk Certified User: Revit Architecture$281.25

Autodesk Certified User: Fusion 360$281.25

Applied Generative AI Specialization$6,250

IBM Generative AI Engineering Professional Certificate

AI for Mechanical Engineers Specialization

Generative AI for Product Managers Specialization

Artificial Intelligence in Finance and Wealth Management Specialization