Japan Store
Manufacturing Analytics
Course Description
Machine Learning can be deployed to 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.
Learning Outcomes
At the end of this course, you should be able to:
- Explain the benefits of machine learning in manufacturing.
- Describe the common operations in developing machine learning applications.
- Apply machine learning for manufacturing analytics.
Course Schedule and Duration
Below is the course schedule. It is recommended that you work through one module per week. The entire course is estimated to take 15 hours to complete. Each module is estimated to take 3.75 hours to complete. Learners will be awarded a course completion certificate upon submitting the four module knowledge checks with an 80% completion score. The certificate is delivered within the Awards tool in the Brightspace course. *Schedule and assignments are subject to change. Any changes will be posted in Brightspace.
In the Brightspace account select Awards from the Course Tools drop-down menu in the navigation bar to view your certificate. When you have completed the course, please select the Course Feedback Survey link to take the anonymous survey. The survey should not take more than 5 minutes to complete. Your feedback will help shape the overall design of this and other online courses offered by Purdue University. When you have completed the courses, please select the Course Feedback Survey link to take you to the anonymous survey. The survey should not take more than 5 minutes to complete.
|
Module |
Topic & Readings |
Quiz |
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1 – Artificial Intelligence in Manufacturing Topics: Smart Manufacturing Artificial Intelligence Applications, Benefits, and Challenges
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Manufacturing Evolution Readings: -What are the 11 Pillars of Industry 4.0 -The Evolution of the Factory-From the Blacksmith to Modern Smart Factories
11 Pillars of Smart Manufacturing
Artificial Intelligence
Readings: -Your Five-Minute Guide to Understanding Machin Learning -A Five-Minute Guide to Artificial Intelligence
|
1 |
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2– Vehicle MPG Prediction Topics: Load & Process Mfg Data Work on Linear Regression Models Artificial Intelligence Perform feature selection on mfg. data
|
Introducing Google Colab Load Manufacturing Dataset Proces Manufacturing Dataset Calculate the Pearson’s Correlation Coefficients Readings: Review and Practice the Project in Colab |
2 |
|
3 – Used Car Price Prediction Topics: Load & Process Mfg Data Compare Various Regression Models Perform Feature Selection on Mfg. Data
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Load Mfg Dataset Process Mfg Data Set Develop a multivariate linear regression model Visualize feature coefficients Develop a polynomial regression model Develop a ridge regression model Perform feature selection with embedded method Perform feature selection with forward selection Readings: Review & Practice the Project in Colab
|
3 |
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4 – Quality Inspection of Casting Products Topics: Load & visualize mfg. data Process mfg. data Build, train & evaluate a Cnn model Augment dataset
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Lading mfg. dataset Visualize mfg. dataset Create the train, validation, and test sets Inspect a batch in the train set Visualize images in a batch of the train set Build a CNN model Train the CNN model Evaluate the CNN model Create an augmented dataset Build, train, and evaluate a CNN model on an augmented dataset Readings: Review and Practice the Project in Colab |
4 |
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