WSQ Principles of machine learning (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The Principles of Machine Learning module covers applied machine learning across five instructional units using Azure Machine Learning. Learners begin with Azure Machine Learning and AutoML, then build regression models for prediction, classification models for categorisation, and clustering models for segmentation, before applying natural language processing to analyse text data.
Practical work is based on Azure Machine Learning, including Azure ML Designer, Azure ML Studio, and AutoML, and is consolidated through a module project that trains regression, classification, and clustering models in Azure ML Designer, spanning sleep-hours prediction, learning-disability classification, student-performance segmentation, and sentiment analysis on text.
The module suits fresh graduates and working professionals moving into machine learning and data science roles. On completion, learners are prepared for job roles such as Junior Machine Learning Engineer, Junior Data Scientist, AI/ML Associate, and Data Analyst.
What You'll Learn
Instructional Units
1. Introduction to Azure Machine Learning and AutoML
2. Regression tasks
3. Classification tasks
4. Clustering tasks
5. Natural Language Processing with Azure Machine Learning
Knowledge Statements:
• Describe the Azure Machine Learning workflow and how AutoML automates model training.
• Explain how regression algorithms predict continuous numerical outcomes from data.
• Explain how classification algorithms assign data to discrete categories.
• Identify clustering techniques used to group unlabelled data by similarity.
• Describe natural language processing methods for analysing text in Azure Machine Learning.
Skills Statements:
• Build and run machine learning experiments in Azure ML Designer using AutoML.
• Train regression models to predict continuous values such as sleep hours.
• Train classification models to predict categorical outcomes from labelled data.
• Apply clustering algorithms to segment data into meaningful groups.
• Perform analysis on text data using Azure Machine Learning.
Entry Requirements
Academic Qualification - Minimum one credit in O level
English Proficiency - Minimum IELTS 5.5 or its equivalent