Singapore University of Social Sciences

AWS Certified Machine Learning

AWS Certified Machine Learning (ICT369)

Applications Open: 01 May 2024

Applications Close: 15 June 2024

Next Available Intake: July 2024

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1529.14 View More Details on Fees

Area of Interest: Science and Technology

Schemes: Alumni Continuing Education (ACE)

Funding: To be confirmed

School/Department: School of Science and Technology


ICT369 AWS Certified Machine Learning aims to equip students with the skills and practical experience needed to select and apply machine learning services to resolve business problems. Students will learn to label, build, train, and deploy a custom machine learning model through a guided, hands-on approach. The labs and learning resources provide students with hands-on experience implementing a machine learning pipeline, using managed machine learning services for forecasting, computer vision and natural language processing. This course will prepare students to take the AWS Certified Machine Learning - Specialty Certification exam.

Level: 3
Credit Units: 5
Presentation Pattern: Every semester


  • Introducing Machine Learning
  • Implementing a Machine Learning pipeline with Amazon SageMaker
  • Introducing Forecasting
  • Introducing Computer Vision
  • Preparing custom datasets for computer vision
  • Introducing Natural Language Processing
  • Processing Text for NLP
  • Implementing Sentiment Analysis
  • Introducing Information Extraction
  • Introducing Topic Modeling
  • Working with Languages
  • Transcribing and vocalizing text with AWS services

Learning Outcome

  • Describe machine learning (ML) concepts.
  • Illustrate managed Amazon ML services for forecasting, computer vision, and natural language processing.
  • Evaluate various algorithms and approaches for a given NLP problem.
  • Create a machine learning pipeline using Amazon SageMaker.
  • Implement solutions to different NLP problems using AWS ML services.
  • Build and run an ML pipeline on AWS for an NLP-specific business problem.
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