Implement Manufacturing Data Mining Techniques

At the end of the course, the learner will be able to::
Identify potential data mining applications for your company.Design a data cube and a data schemaApply hierarchical clustering technique for quality control.Apply K-means clustering technique for quality controlMeasure correlation and dependency between process variablesBuild linear regression modelEvaluate regression model accuracy and model coefficient significanceBuild ARMA modelDevelop regression mode and ARMA model for semi-con equipment automolding processDevelop a neural network using feed forward propagationDevelop a neural network using back propagation learningDevelop neural network for predicting process yield

What You'll Learn
This course on Implement Manufacturing Data Mining Techniques aims to provide students with a good understanding of fundamental data warehouse and data mining techniques for different manufacturing process applications. The students will learn how to use data mining techniques like multiple regressions, data clustering, neural networks to develop models for process or equipment performance data analysis. Real-life case studies will be used to illustrate the concepts and methodologies. Class learning exercises and hands-on sessions are also included for students to fully understand the data mining techniques in analysis of manufacturing process performance.

Minimum Entry Requirement
Diploma with relevant experience or a degree