brain and code graphic

Graduate Certificate in AI Foundations for Product Innovation

An online graduate-level program in AI & Machine Learning designed for working professionals

Advances in artificial intelligence are already changing the way business works—including product innovation and development processes.

Be part of this revolution through Duke’s online Graduate Certificate in AI Foundations for Product Innovation. The certificate is a standalone, credit-bearing non-degree offering aimed at professionals who want to keep working and keep learning.

Gain valuable skills in applying machine learning and artificial intelligence to build or enhance products in your industry. Apply your new knowledge at work or position yourself for new opportunities in one of the hottest fields.

The certificate can be completed in 4 semesters—without the commitment required of a traditional degree program.

Get Started

Who Should Apply

  • Those eager to gain new skills related to AI and Machine Learning
  • Professionals looking to learn new, relevant skills while still working
  • Those with at least a bachelor's degree in engineering or science—and at least a semester's experience in computer programming

What You’ll Learn

A great education! You'll develop a strong grasp of AI and Machine Learning fundamentals—as well as the business, policy, and ethical considerations of implementing them.

Also, as a Duke online student you will:

  • Experience online courses taught by expert Duke faculty and industry practitioners
  • Build a portfolio of real-world, hands-on projects
  • Receive individualized course advising
  • Be engaged with outstanding peers around the world and with our faculty
  • Be invited to attend an optional on-campus workshop and reception
  • Earn credits toward a Master of Engineering in AI for Product Innovation, if you decide to continue learning at Duke

[Quote or fact related to the industry need for engineers or scientists who know and understand how AI can be applied to product development]


Click for course description

510: Sourcing Data for Analytics

Course Description: In industry, one of the main activities, and challenges, of implementing machine learning applications is collecting data to use in modeling. This course introduces students to both the technical and non-technical (business, regulatory, ethical) aspects of collecting, cleaning, and preparing data for use in machine learning applications. The first segment of the course will be an introduction to numerical programming focused on building skills in working with data via the Numpy and Pandas libraries, two of the most common tools used by teams working with data and modeling. Technical aspects covered will include the types of data, methods of sourcing data via the web, APIs, and from domain-specific sensors and hardware (IoT devices), an increasingly common source of analytics data in technical industries. The course also introduces methods and tools for evaluating the quality of data, performing basic exploratory data analysis, and pre-processing data for use in analytics. Non-technical aspects covered include an introduction to data privacy, GDPR, regulatory issues, bias, and industry-specific concerns regarding data usage.

520: Modeling Process & Algorithms

Course Description: This course is an introduction to the modeling process and best practices in model creation, interpretation, validation, and selection of models for different uses. The primary machine learning algorithms, both supervised and unsupervised, are introduced and explained with the necessary level of mathematical theory to establish students’ intuition for how each algorithm works. The primary focus will be on “traditional” machine learning approaches but it will also introduce deep learning and its applications. At the end of this course, students should have a solid understanding of the end-to-end modeling process and the different types of model algorithms along with the strengths, weaknesses, assumptions, and use cases for each type.

530: Applying AI in Practice

Course Description: This course focuses on the implementation via programming of the data pre-processing and machine learning modeling process, taught in a case-based format where students gain experience in applying the concepts learned in the other Fall core courses (Sourcing Data for Analytics, and Modeling Process & Algorithms) on real-world scenarios in industrial domains. The course will be taught in Python, as the language used today by the vast majority of teams in industry working with data modeling. The content will consist of case studies drawing on real-world, often messy datasets that will reinforce students’ programming skills in cleaning, exploring and visualizing data, applying machine learning algorithms, and interpreting and validating results in the context of solving business problems. Programming assignments will expose students to several of the commonly used existing tools and libraries for implementing ML.

540: Building Products Using Deep Learning

Course Description: This course builds on the previous semester core courses by bringing together the various aspects of a machine learning-based product implementation, including opportunity identification, data sourcing, business planning, and model prototyping and evaluation. The course is intended to be hands-on and includes a significant team project prototyping the use of a deep learning model within a new or existing industry product/service to create an additional value-add for the product. In addition to project-based learning, a number of industry-focused case examples will be used to strengthen each student's understanding of the applications for deep learning, including natural language processing, computer vision, and analysis of structured and time-series data. The course will reinforce students’ skills across all aspects of the product development process using AI and will build a solid understanding of the applications of deep learning in particular.

Expert Duke Faculty

The online courses are taught by Duke faculty members with industry experience—and who teach these same courses in classrooms on the Duke campus.

Jon Riefschneider

Jon Riefschneider

Program Director and Executive in Residence

A former technology executive who was responsible for launching predictive analytics products on which over half of major US electric utilities and global airlines depend.

Luis Morales

Luis Morales

Programming Faculty

Twenty-eight years of experience in technical leadership roles at Cisco Systems and AT&T Labs. Winner of Cisco’s highest technical recognition.

Daniel Egger

Daniel Egger

Data & Analytics Faculty

Founder and CEO of a series of venture-backed technology companies before joining the Duke faculty.

Certificate Program Details


The program is open to qualified applicants worldwide.

In general, applicants should:

  • Hold at least a bachelor’s degree in engineering or science
  • Have at least one semester's experience in programming in any language, Python preferred
  • Expect to provide a statement of purpose, resume, letters of recommendation, and to participate in an admissions interview

Application requirements »


Tuition is paid per course. A limited number of scholarships are available, covering 20 percent of tuition expenses.

Tuition information »

Get Started

Click below to contact our admissions team for more information. We look forward to hearing from you!

Contact Admissions