AI Product Management

Overview

Syllabus

Courses

• 1 review

  17 hours 19 minutes

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  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
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  14 hours 57 minutes

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  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
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  18 hours 6 minutes

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  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

Taught by

Jon Reifschneider

Tags

• united states