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Artificial Intelligence Micro Credentials
Purdue University’s Artificial Intelligence Microcredentials offer quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. Every course functions as its own mini-credential. Students earn a certificate of completion and digital badge from Credly for every course they complete. Students can pick and choose what course to take and stack credentials in topics that interest them.
Several of these courses have been recognized by ABET under its Certificate Recognition Standards. The certified courses include: Machine Learning for Linear Predictive Models, Data Analytics for Decision Makers, Manufacturing Analytics, Natural Language Processing Solutions, Demystifying AI, Understanding Risks, and Shaping the Future, Global Regulation of AI, Data and AI Storytelling, Prompt Engineering, AI Policy and Governance, and Machine Learning in Action.
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Overview
Join the AI Revolution
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. 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.
Learn essential AI skills, including:
- Understanding the current applications of AI and where the field is heading.
- How to apply AI technologies to solve real-world problems and contribute to organizational innovation.
- How to amass a robust AI skillset and build marketable expertise in emergent technologies.
Lay the groundwork for your journey with Purdue’s core AI technical bundle. Strengthen your understanding of data analytics, essential math concepts and machine learning fundamentals. Through these courses, you’ll gain the analytical and coding literacy to interpret algorithms, work with data and prepare for advanced AI applications.
Learn essential skills such as:
- Interpret data sets, trends and model outputs using analytical frameworks.
- Apply core math and statistics principles to machine learning problems.
- Translate algorithmic concepts into practical, real-world understanding.
Courses in bundle include:
- Machine Learning for Linear Predictive Models
- Essential Math Tools for AI: A Practical Refresher
- Machine Learning Fundamentals: Principles and Applications
- Data Analytics for Decision Makers
Take your AI skills from concept to execution. This hands-on bundle is designed for professionals who want to implement machine learning models and AI solutions in real-world scenarios. Dive into courses to learn how to design, deploy and evaluate AI-driven tools that solve practical business and industrial challenges.
Learn essential skills such as:
- Design and deploy machine learning and natural language processing models.
- Evaluate real-world AI use cases and measure performance outcomes.
- Integrate AI solutions into workflows for manufacturing, analytics and data-driven operations.
Courses in bundle include:
- Manufacturing Analytics
- Machine Learning in Action: Tools, Techniques, and Industrial Cases
- Natural Language Processing Solutions: An Introduction to Evaluation and Implementation
AI is changing every industry, and leaders must understand how to guide its impact. This bundle is designed for professionals, educators and policymakers who want to explore AI’s opportunities without needing to code. The courses teach you how to communicate AI concepts, understand regulatory frameworks and make informed decisions about responsible innovation.
Learn essential skills such as:
- Communicate AI concepts clearly to technical and nontechnical audiences.
- Analyze policy, ethics and governance frameworks shaping AI adoption.
- Lead organizational or classroom discussions on responsible and strategic AI use.
Courses in bundle include:
- Demystifying AI, Understanding Risks, and Shaping the Future
- AI Policy and Governance
- Global Regulations of AI
- Prompt Engineering
- AI Data Storytelling Primer
- Interdisciplinary AI Fundamentals
AI is revolutionizing hundreds of industries, and AI skills are some of the most in-demand job skills in today’s tech-driven market. Purdue University’s Artificial Intelligence Microcredentials offer quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. Every course functions as its own mini-credential, and students earn a certificate for every course they complete. Courses are also taught by the well-renowned faculty of Purdue.
Learn essential skills such as:
- Understanding the current applications of AI and where the field is heading.
- How to apply AI technologies to solve real-world problems and contribute to organizational innovation.
- How to amass a robust AI skillset and build marketable expertise in emergent technologies.
Courses in bundle include:
- Machine Learning for Linear Predictive Models
- Essential Math Tools for AI: A Practical Refresher
- Machine Learning Fundamentals: Principles and Application
- Data Analytics for Decision Makers
- Manufacturing Analytics
- Machine Learning in Action: Tools, Techniques, and Industrial Cases
- Natural Language Processing Solutions: An Introduction to Evaluation and Implementation
- Demystifying AI, Understanding Risks, and Shaping the Future
- AI Policy and Governance
- Global Regulations of AI
- Prompt Engineering
- AI Data Storytelling Primer
- Interdisciplinary AI Fundamentals
Explore Our 100% Online Courses
Customize your studies to fit your career goals
Every course in the AI Micro-credentials program functions as its own mini-credential, and students earn a certificate for every course they complete. Students can pick and choose what courses to take and stack credentials in topics that interest them. The flexibility of this program makes it an ideal upskilling opportunity for tech professionals.
Foundational – Technical Courses
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Review of linear algebra: To review basic concepts in linear algebra including norm, inner product, linear combination, basis functions, matrix-vector multiplication, eigen decomposition
– Principles of regression: To understand the basic principles of regression, including loss function, linear models, parameter update
– Solving a regression problem: To derive the linear least squares solutions and understand the properties under-determined and over-determined linear systems
– Regularization techniques: To apply ridge regression techniques and LASSO regression techniques
– Optimization: To review constrained and unconstrained minimization, Lagrange multiplier, convexity, gradient descent, and stochastic gradient descent
| Module | Topic & Readings |
| Module 1 | Why do we need linear algebra in machine learning? Inner products and norms Matrix Calculus Eigenvalues and eigenvectors Principal Component Analysis Eigenface Problem |
| Module 2 | What is regression? How does regression work? Solving Linear Regression and Matrix-vector form of linear regression |
| Module 3 | Overdetermined and undetermined lease squares Robust Linear Regression Solving the Robust Regression Problem |
| Module 4 | Ridge Regression and Implementation LASSO Regularization LASSO for Overfitting |
| Module 5 | Convexity Lagrange and Multiplier Solving Simple Constrained Optimization Gradient Descent Convergence and Momentum Acceleration |
Faculty: Stanley Chan
Course Description: 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.
Prerequisites:
Basic probability
Linear algebra
Ordinary differential equations (knowledge of ordinary differential equations will be valuable but is not required in order to be successful)
Learning Outcome:
– Analyze equations involving matrices by applying algebraic concepts such as rank, nullspace, linear independence, and eigenvalues
– Define properties of linear systems, including controllability, observability, and stability, and apply them to design state estimators and feedback controllers
– Define probability distributions and moments of random variables, and characterize the long-term behavior of stochastic processes
– Specify the fundamental optimality conditions for optimization problems, and implement basic algorithms to find the optimizers
| Module | Topic & Readings |
| Module 1 | Vectors and Matrices System of Equations and Eigenvalues Diagonalization and Definite Matrices Norms |
| Module 2 | Basics and Graph Properties Search Algorithms and Trees Shortest Paths |
| Module 3 | Basics and Stability Controllability and Observability Lyapunov Theory |
| Module 4 | Basics and Conditional Probability Random Variables and Expectation Markov Chains |
| Module 5 | Extrema and Optimality Infimum and Supremum Convexity and Algorithms |
Faculty: Philip E. Paré and Shreyas Sundaram
Course Description: This course provides in-depth conceptual explanation of supervised and unsupervised machine learning algorithms and how to implement them to address real-world problems.
Prerequisites: None
Learning Outcome:
– Assess and understand the core principles, applications, and limitations of machine learning, distinguishing its role from traditional programming
– Analyze a variety of supervised and unsupervised machine learning algorithms using prominent frameworks
– Integrate machine learning knowledge to address real-world challenges by choosing appropriate algorithms and techniques
| Module | Topic & Readings |
| Module 1 | Intro to Machine Learning Understand and Build Machine Learning Models |
| Module 2 | Supervised Machine Learning Algorithms Logistic and Linear Regression |
| Module 3 | Understand Machine Learning Algorithms Applications of Clustering |
| Module 4 | Capstone Project |
Faculty: Jin Kocsis
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Examine foundational concepts in data mining
– Differentiate between descriptive and predictive elements of data mining
– Contrast the strengths and weaknesses of supervised and unsupervised methods
| Module | Topic & Readings |
| Module 1 | Foundations of Data Mining Data Concepts Data Quality |
| Module 2 | Components of Data Mining Pattern Recognition Visualization and Large-Scale Data |
| Module 3 | Methods for Data Mining Supervised Machine Learning Deep Learning |
Faculty: John Springer
Applied – Technical Courses
Course Description: Machine Learning can be deployed in manufacturing to significantly increase production efficiency and capacity. In this course, step-by-step tutorials on how to apply machine learning to analyze manufacturing data are presented. Students will learn how to create artificial intelligence solutions for manufacturing analytics.
Prerequisites: None
Learning Outcome:
– Explain the benefits of machine learning in manufacturing
– Describe the common operations in developing machine learning applications
– Apply machine learning for manufacturing analytics
| Module | Topic & Readings |
| Module 1 | Smart Manufacturing Artificial Intelligence Applications, Benefits, and Challenges |
| Module 2 | Vehicle MPG Prediction Load and Process Manufacturing Data Work on Linear Regression Models |
| Module 3 | Used Car Price Prediction Compare Various Regression Models Perform Feature Selection on Manufacturing Data |
| Module 4 | Quality Inspection of Casting Products Build, Train, and evaluate a CNN Model Augment Dataset |
Faculty Name: Xiumin Diao
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Course Description: This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on real-world examples in Python. The overall aim is to provide the students with a good understanding of machine-learning technologies, building machine learning with Python, and applying machine-learning technologies to address real-world problems.
Prerequisites: None
Learning Outcome:
– Explain the relationship (main mechanisms, internal logic, computing components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoder, General Adversarial Network (GAN), and Reinforcement Learning (RL))
– Program the basic realization of the machine learning models, stated in Learning Objective 1, in Python
– Apply the eight machine learning models stated in Learning Objective 1 to solve real-world problems
| Module | Topic & Readings |
| Module 1 | What is Machine Learning What is the Insight of Machine Learning |
| Module 2 | Linear Regression Application Linear Regression Implementation |
| Module 3 | Logistic Regression Formulation and Implementation Solving the Robust Regression Problem |
| Module 4 | Introduction of Neural Networks Application Fully Connected Neural Network Implementation |
| Module 5 | Convolutional Neural Network Application Convolutional Neural Network Implementation |
| Module 6 | Recurrent Neural Network Application Recurrent Neural Network Implementation |
| Module 7 | Auto-Encoder Application and Implementation Generative Adversarial Network Application and Implementation Deep Reinforcement Learning Models Application and Implementation |
Faculty: Jin Kocsis
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Describe the capabilities of existing NLP systems
– Analyze the gap that exists between a stated scenario and the existing capabilities of NLP systems
– Test solutions by measuring improvements introduced by NLP systems
| Module | Topic & Readings |
| Module 1 | History of Natural Language Processing Words vs Concepts and Explicit vs Implicit General Domain and Specific Domain |
| Module 2 | Shallow NNs Contextual Embeddings LM Capabilities |
| Module 3 | Testing in General Factual Correctness and Reasoning Intro into Prompt Learning and Engineering |
| Module 4 | Capstone Reflection Project |
Faculty: Julia Rayz
Foundational – Non-Technical Courses
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Explain the history of AI research and why deep learning became the dominant approach
– Explain, in a non-technical way, how deep learning works
– Outline various risks of AI, including both speculative dangers and more tangible and immediate risks to the economy, the labor market, and civil society
| Module | Topic & Readings |
| Module 1 | History of Artificial Intelligence |
| Module 2 | A Crash Course on Deep Learning |
| Module 3 | The Singularity and Other Speculative Fears |
| Module 4 | The Immediate Risks of Artificial Intelligence |
Faculty: David Peterson
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Identify core concepts in the emerging AI policy domain including key actors, institutions, and governance strategies that have been proposed by or adopted in governments, firms, and civil society organizations
– Analyze and evaluate the social and ethical dimensions of AI, focusing on issue spotting and understanding the policy implications of these issues
– Examine current regulatory trends and policy frameworks for AI governance, including prominent debates and challenges
– Synthesize emerging best practices, issues, and debates in AI policy and governance, while developing strategies for continual monitoring and staying informed
| Module | Topic & Readings |
| Module 1 | Introduction to AI |
| Module 2 | Key Concepts in International Governance |
| Module 3 | Corporate Governance and Self-Regulation |
| Module 4 | Ethics and Impact Assessment |
| Module 5 | Getting Engaged and Building Expertise |
Faculty: Daniel Schiff
Course Description: 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.
Prerequisites: None
Learning Outcome:
– Analyze the demand for global regulation of AI
– Identify key issues for AI regulation to address in a global context
– Explain leading hard and soft models for global AI regulation
| Module | Topic & Readings |
| Module 1 | Demand for Global AI Regulation |
| Module 2 | Core Issues for Global Regulation for AI |
| Module 3 | Hard Regulation: The European Model |
| Module 4 | Soft Regulation: Industry Standards |
Faculty: Dr. Swati Srivastava
Course Description: Explore the emerging field of prompt engineering with AI tools like large language models (LLMs) (ChatGPT, Claude, Gemini, etc.) and image models (Midjourney, DALL-E, etc.) and learn to harness the power of Generative AI to improve daily life, work, and learning experiences. This asynchronous, online course offers a practical introduction to AI and LLMs through interactive content, short videos, practical exercises, and self-assessments. This course will help non-technical learners to understand AI, use AI tools effectively, and craft prompts that enhance AI’s utility in various applications. By engaging with this course, learners will gain skills to “talk” to AI tools, effectively becoming programmers of their digital interactions.
Prerequisites: None
Learning Outcomes
-Apply in-demand prompt engineering skills for Generative AI tools.
-Create and design prompts using the cutting-edge AI tools like LLMs and Stable Diffusion models.
| Module | Topic & Readings |
| Module 1 | Introduction to AI and Language Models |
| Module 2 | Interacting with Language Models |
| Module 3 | Crafting Effective Prompts |
| Module 4 | Practical Applications of Prompt Engineering |
Faculty: Elsayed Issa
Course Description: In this course, students will learn how to create engaging data stories by contradicting common perception. They will use data analysis and AI prompts using Chat GPT to produce effective data stories and justify why AI makes these data stories more effective.
Prerequisites: None
Learning Outcome:
– Identify how data stories should surprise, provide a new, more convincing explanation for time-worn ideas
– Identify ways to tell memorable, teachable arguments in the form of stories
– Examine how AI tools may be used to satisfy these conditions of storytelling
| Module | Topic & Readings |
| Module 1 | Introduction |
| Module 2 | What is Research? |
| Module 3 | Where Do Stories Come From? |
| Module 4 | Pictures Are Not Stories |
| Module 5 | AI & Storytelling |
| Module 6 | Conclusion |
Faculty: Sorin Matei
Course Description: This transdisciplinary course demystifies artificial intelligence by exploring its technological evolution, system design principles, and practical applications—making complex AI concepts accessible to learners from any background. You’ll develop essential skills for understanding, conceptualizing, and communicating about AI systems, gaining critical knowledge that applies across industries and disciplines.
Prerequisites: None
Learning Outcome:
– Describe the foundations of artificial reasoning, knowledge, and trace the historical development of AI
– Explain the basic concepts, terminologies, and applications of different AI algorithms, developing a problem-solving mindset applicable to various domains
– Examine the diverse range of AI applications across varied industries
– Explain the role human factors and social context play in the development of artificial intelligence
| Module | Topic & Readings |
| Module 1 | Historical Development and Current Trends in AI |
| Module 2 | Role of Translators and Builders in AI – Knowledge, Reasoning & the Connection to Artificial Intelligence |
| Module 3 | AI Alignment and Domain Applications |
Faculty: Bill Watson, Xingyu Li, and Cherie Maestes
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