Duke University
AI Product Management Specialization
Duke University

AI Product Management Specialization

Manage the Design & Development of ML Products. Understand how machine learning works and when and how it can be applied to solve problems. Learn to apply the data science process and best practices to lead machine learning projects, and how to develop human-centered AI products which ensure privacy and ethical standards.

Jon Reifschneider

Instructor: Jon Reifschneider

Included with Coursera Plus

Get in-depth knowledge of a subject
4.7

(797 reviews)

Beginner level

Recommended experience

4 months to complete
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.7

(797 reviews)

Beginner level

Recommended experience

4 months to complete
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Taught in English
15 practice exercises

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Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Duke University

Specialization - 3 course series

What you'll learn

Skills you'll gain

Regression Analysis, Supervised Learning, Machine Learning, Deep Learning, Performance Metric, Algorithms, Artificial Neural Networks, Random Forest Algorithm, Unsupervised Learning, Decision Tree Learning, Artificial Intelligence and Machine Learning (AI/ML), Computer Vision, Data Science, Predictive Analytics, Natural Language Processing, and Classification And Regression Tree (CART)

What you'll learn

Skills you'll gain

Machine Learning, Technical Design, System Monitoring, Data Pipelines, Data Collection, Data Cleansing, Feature Engineering, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Project Management, Data Quality, Data Management, Data Science, Systems Design, Technical Management, Systems Architecture, Software Development Life Cycle, Technology Solutions, MLOps (Machine Learning Operations), and Data Processing
Human Factors in AI

Human Factors in AI

Course 317 hours

What you'll learn

  • Identify and mitigate privacy and ethical risks in AI projects

  • Apply human-centered design practices to design successful AI product experiences

  • Build AI systems that augment human intelligence and inspire model trust in users

Skills you'll gain

Artificial Intelligence, Human Factors, Responsible AI, Data Ethics, Human Centered Design, Information Privacy, User Experience Design, Automation, Personally Identifiable Information, Law, Regulation, and Compliance, Machine Learning, Human Learning, Design Thinking, and Human Computer Interaction

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Jon Reifschneider
Duke University
3 Courses79,528 learners

Offered by

Duke University

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