Course Overview Print

Across industries such as healthcare, finance, logistics, and the public sector, organisations increasingly rely on accurate data collection and analysis to improve performance, optimise operations, and support policy and strategic decisions. Through a practical exploration of machine learning workflows, participants will develop competencies in identifying relevant data sources, preparing and structuring datasets, and applying analytical techniques to generate meaningful insights. This course strengthens employability by building job-relevant capabilities in data collection, data preparation, and analysis, which are core requirements in many digital and transformation roles.

Learning Outcomes:

  • Apply data collection and preparation techniques within machine learning workflows, including feature engineering methods to ensure data quality for analysis.
  • Interpret data-driven insights and evaluation metrics to support informed decision-making.
  • Deploy generative AI applications by applying prompt engineering techniques and integration methods for organizational use cases.

Course Outline

Module 1: The Reality of ML

  • Overview of ML applications and the role of AI Engineering
  • The AI Lifecycle: Moving beyond "code" to scoping and strategy

Module 2: Data Engineering & Feature Engineering

  • The Data-Ready workshop
  • Feature Selection Vs Feature Engineering

Module 3: Supervised Learning Fundamentals

  • Supervised Learning (Regression)
  • Supervised Learning (Classification)

Module 4: Unsupervised & Reinforcement Learning

  • Clustering (K-Means) and Dimensionality Reduction (PCA).
  • Reinforcement Learning: Conceptual overview of agents and rewards

Module 5: Model Evaluation & Performance Metrics

  • Moving beyond Accuracy: Confusion Matrix, Precision, Recall, F1 Score, and ROC Curves.

Module 6: Ethics, Bias & Fairness

  • The Ethical AI: Identifying algorithmic bias in training data.
  • Explainable AI (XAI) principles for stakeholders.

Module 7: Introduction to Generative AI Applications

Module 8: Generative AI Deployment & MLOps

  • Deployment: Exporting models using Pickle/Joblib.

Target Audience and Pre-requisites

This course is designed for:

  • Data Analysts and Aspiring Data Scientists
  • Machine Learning and Software Engineers
  • Technology Consultants and Solution Architects
  • Technical Leads and Managers overseeing AI/ML initiatives
  • Professionals seeking a practical, lifecycle‑focused understanding of machine learning rather than algorithm‑only training

Minimum entry requirement

Working Experience: Minimum working experience of 1 month and basic programming knowledge in Python.

Minimum Education: Diploma - Age ≥ 20 years old - Workplace Literacy and Numeracy Level 5 (WPLN: Speaking, Writing, Listening, Reading, and Numeracy at ESS level 5 or equivalent)

Learners will be advised to attend basic python course if they are unable to meet the minimum entry requirements.

SWDA Funding Requirements

  • Trainees must scan their attendance twice daily using the SingPass application.
  • Trainees must attain at least 75% attendance.
  • Trainees must pass the in-house assessment to be eligible for funding.
  • Trainee and/or sponsoring company is/are required to meet all SWDA-mandated eligibility criteria and requirements for funding.  For more information, please refer to SkillsFuture homepage.

Appeal Policy and Procedure

  • As a candidate in this course assessment, you may appeal your results if you disagree with them.
  • To do so, submit your written appeal request via email to esv_comat_cse@stengg.com within 3 working days from date of assessment.

Cancellation, Postponement and Refund Policy

  • Request for cancellation or postponement must be submitted in writing more than 4 weeks before the class start date to avoid any charges.
  • Written notice for cancellation or postponement received 2 to 4 weeks before class start date will incur Late Cancellation Charge - 50% of course fee.
  • Written notice for cancellation or postponement received less than 2 weeks before class start date will incur Late Cancellation Charge - 100% of course fee.
  • If payment has been made and ST Engineering e-Services Pte Ltd accepts the trainee's written notification to cancel or withdraw from the course, ST Engineering e-Services Pte Ltd will issue a refund, less any applicable Late Cancellation Charges.

Feedback Policy and Procedure

  • You may submit feedback via email to esv_comat_cse@stengg.com or your servicing Account Manager.
  • Any formal feedback will be handled within 10 working days from receipt with a written reply given.  An interim reply will be provided should more time be required.
*Important Note : Fees are subject to Singapore's prevailing Goods and Services Tax (GST).

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Applied Machine Learning - From Data to Deployment
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