AI Microcredentials Applied Technical Bundle
Learn how to apply AI in real-world contexts. Explore Manufacturing Analytics, NLP Solutions and Machine Learning in Action. Develop the skills to design, test and deploy practical AI models.
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Overview
Unlock the power of data with Purdue’s Artificial Intelligence Microcredentials bundle
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. Courses are also taught by the well-renowned faculty of Purdue.
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:
- 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.
The cost of attending Purdue varies depending on where you choose to live, enrollment in a specific program or college, food and travel expenses, and other variables. The Office of the Bursar website shows estimated costs for the current aid year for students by semester and academic year. These amounts are used in determining a student’s estimated eligibility for financial aid. You can also use our tuition calculator to estimate tuition costs.
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Customize your studies to fit your career goals
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 such as Manufacturing Analytics, Natural Language Processing Solutions, and Machine Learning in Action to learn how to design, deploy and evaluate AI-driven tools that solve practical business and industrial challenges.
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
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