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Course Overview

This course will equip participants with the technical skills to apply machine learning algorithms and deploying practical Artificial Intelligence (AI) models with an industrial edge controller for anomaly detection and predictive maintenance. The course will also cover the essential concepts and background for data analysis on supervised and unsupervised machine learning models and their applications in advanced manufacturing. They will be exposed to the manufacturing AI industry use cases deployed at the smart factory within Temasek Polytechnic Advanced Manufacturing Centre.

Entry Requirements

Participants should have relevant working experiences in manufacturing sector.

Who Should Attend

Engineers, Technology Specialist

What You'll Learn

Perform data processing in machine learning

  • Identify data types and categories
  • Describe data collection protocols
  • Perform data transformation and editing 
  • Apply feature selection on data sets
  • Examine outliers and unbalanced data sets

Apply classification analysis

  • Explain the logistic regression method
  • Examine the prediction outcome in classification
  • Develop different types of classifiers
  • Illustrate optimising methods and performance evaluation in classification
  • Apply decision tree and decision forest learning
  • Apply support vector machine
  • Apply regression analysis in advanced manufacturing

Apply Linear Regression

  • State the standard metrics in regression analysis 
  • Explain the linear regression method
  • Develop different types of linear regression models 
  • Illustrate optimising methods and performance evaluation
  • Apply decision tree learning with threshold
  • Apply regression analysis in advanced manufacturing

Describe the fundamental concepts of edge computing and machine learning

  • Explain the fundamental of edge computing 
  • Explain the fundamental of supervised machine learning
  • Identify the different types of machine learning models 
  • Describe the application of edge computing and machine learning in predictive analytics

Implement machine learning models in edge processor

  • Introduction to the major functional components of an edge AI controller
  • Generate a plan for predictive maintenance
  • Apply tools and libraries to collect data
  • Develop a machine learning model with trained data
  • Deploy a machine learning model to edge processor 
  • Analyse the performance of applied Artificial Intelligence (AI) models in predictive maintenance 





Written test, online quiz, oral assessment










Participants will be issued with a Certificate of Performance upon meeting 75% of the required course attendance.

Career Opportunities

Automation Engineer, Technical Support Engineer, Industrial AI Specialist

Course Schedule/Apply

Intake Info Application Closing Date Course Duration

22 - 23 September 2022

22 August 2022

2 days - Face to Face (9.00am - 5.00pm) and 16 hours of self-paced e-learning


10 – 11 November 2022

10 October 2022

2 days - Face to Face (9.00am - 5.00pm) and 16 hours of self-paced e-learning


Registration may be closed earlier due to overwhelming response.


For Corporate training, click here.


View the application guide here.


Please write to tsa_shortcourse@tp.edu.sg to be included in the course waitlist for future intakes.

Course Fees

Fee Type Item Total Fees (w GST)
Full Course Fee S$1,284.00
Singapore Permanent Residents S$385.20
Singapore Citizens aged 39 & below S$385.20
Singapore Citizens aged 40 & above S$145.20
SME company-sponsored Singapore Citizens & Singapore Permanent Residents S$145.20

SkillsFuture Credit Approved. For more details, please click here.  


With effect from 1 Jul 2020, the Workforce Training Scheme (WTS) will be replaced by the Work Support Scheme (WSS), for more information, please visit:


Lecturer/Trainer Profile

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