Manufacturing Analytics

Member Price: $499.00
Non-Member Price: $549.00

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

1 – Artificial Intelligence in Manufacturing

Topics:

Smart Manufacturing

Artificial Intelligence

Applications, Benefits, and Challenges

 

 

 

 

 

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

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

 

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

4 – Quality Inspection of Casting Products

Topics:

Load & visualize mfg. data

Process mfg. data

Build, train & evaluate a Cnn model

Augment dataset

 

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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