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
Assessment
Written test, online quiz, oral assessment
Level
Basic
Certification
Participants will be issued with a Certificate of Performance upon meeting 75% of the required course attendance.
