Artificial Intelligence Micro-credentials

Purdue’s AI Micro-credentials Program offers quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. With an average completion time of only 15 hours, this program is an ideal upskilling opportunity for professionals who want to advance in their careers quickly.

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Unlock the power of data with Purdue’s Artificial Intelligence Micro-credentials program.

AI is revolutionizing hundreds of industries, and AI skills are some of the most in-demand job skills in today’s tech-driven market.

Learn Essential AI Skills, including:

  • Understanding the current applications of AI and where the field is heading.
  • How to utilize AI technologies in a variety of organizational contexts through completing real-world projects.
  • How to amass a robust AI skillset and build marketable expertise in emergent technologies.
hours average time to complete course
Courses in program
Individual Course Cost

Program Specifics

Learn more about Purdue University’s Artificial Intelligence Micro-credentials

According to Forbes, 97 million jobs related to artificial intelligence (AI) will be created between 2022 and 2025. AI is revolutionizing hundreds of industries, and AI skills are some of the most in-demand job skills in today’s tech-driven market.

Learning Outcome: This course provides a foundation for understanding machine learning and its applications by taking a learn-by-doing approach. Students will learn about machine learning by training a regression model to perform data analysis. 

Faculty: Staff 

Learning Outcome: Students will work through real-world problems and examples to understand the mathematical background for AI. By breaking concepts down and putting them in context, this course makes the math behind AI accessible for a wider audience.  

Faculty: Philip E. Paré and Shreyas Sundaram  

Learning Outcome: This course provides in-depth conceptual explanation of supervised and unsupervised machine learning algorithms and how to implement them to address real-world problems.  

Faculty: Rishikesh P Fulari

Learning Outcome: In this course, students will be able to explore data mining hands-on by using data mining tools for pattern recognition, visualization, artificial intelligence and more.  

Faculty: John Springer  

Learning Outcome: This course teaches students how to understand and explain the benefits of AI in a manufacturing context and is ideal for industry professionals seeking to upskill with AI expertise. It is also ideal for non-industry professionals wanting to build more familiarity with AI.  

Faculty Name: Xiumin Diao  

Learning Outcome: This course gives students a foundational education in machine learning by providing hands-on, real-world examples in Python. Students will learn how to build machine learning with Python, and then apply machine learning to address real-world problems.  

Faculty: Jin Kocsis   

Learning Outcome: This course teaches about the human and social factors that affect artificial intelligence and its applications, showing students how to develop ethical, responsible, and human-centered AI solutions to real-world problems.  

Faculty: Ankita Raturi 

Learning Outcome: This course focuses on the real-world uses of natural language processing systems, including the current capabilities of natural language processing systems and how NLP can be refined and improved.  

Faculty: Julia Rayz   

Learning Outcome: This course provides students with the real-world knowledge they need to navigate the risks of AI and how it’s changing the technology landscape. Students will break AI down into engaging, accessible concepts and explore the ethics of AI through real-world examples.  

Faculty: David Peterson 

Learning Outcome: This course explores the ethical and regulatory framework that underpin AI. Students will analyze real-world policy and governance strategies that seek to manage AI’s impacts and engage in debates that will shape the future of the field.  

Faculty: Daniel Schiff

Learning Outcome: This course covers the growing global demands for AI regulations and puts them in context so students can understand what risks these regulations seek to address and how companies and governments can anticipate and comply with them.  

Faculty: Dr. Swati Srivastava 

Learning Outcome: This course covers the foundations of data visualization and teaches students how to craft compelling and persuasive data stories. Students will use data analysis and visualization principles to work through real-world projects.  

Faculty: Sorin Matei   

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