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This course equips learners with the competencies to apply structured data collection, modelling, and advanced analytics techniques to support data-driven decision-making in organisational contexts. Learners will develop the ability to collect and extract data based on defined requirements, and apply data modelling methods to identify meaningful relationships between variables. The course also covers model deployment, MLOps practices, and ongoing model maintenance, equipping learners with the skills to support end-to-end Machine Learning implementation in an organisational environment. This is an intermediate course.

Learning Outcomes:

  • Apply data collection and analysis techniques to collect and extract data according to defined requirements
  • Apply data modelling techniques to identify relationships between variables
  • Analyse clustering results to identify patterns and exceptions.
  • Assess analytical outcomes generated by neural network models.
  • Evaluate data-driven insights by explaining model results to support informed decision-making.

 

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 SSG-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.

 

Exam Details

This course is designed to build participants’ understanding of key concepts and domains covered in the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.

Participants will explore a comprehensive AI development lifecycle, from data preparation and model building to deployment and maintenance. The course includes the certification exam voucher as part of the course fee.

To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems

Topic B: Formulate a Machine Learning Problem

Topic C: Select Approaches to Machine Learning

 

Lesson 2: Preparing Data

Topic A: Collect Data

Topic B: Transform Data

Topic C: Engineer Features

Topic D: Work with Unstructured Data

 

Lesson 3: Training, Evaluating, and Tuning a Machine Learning Model

Topic A: Train a Machine Learning Model

Topic B: Evaluate and Tune a Machine Learning Model

 

Lesson 4: Building Linear Regression Models

Topic A: Build Regression Models Using Linear Algebra

Topic B: Build Regularized Linear Regression Models

Topic C: Build Iterative Linear Regression Models

 

Lesson 5: Building Forecasting Models

Topic A: Build Univariate Time Series Models

Topic B: Build Multivariate Time Series Models

 

Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor

Topic A: Train Binary Classification Models Using Logistic Regression

Topic B: Train Binary Classification Models Using k-Nearest Neighbor

Topic C: Train Multi-Class Classification Models

Topic D: Evaluate Classification Models

Topic E: Tune Classification Models

 

Lesson 7: Building Clustering Models

Topic A: Build k-Means Clustering Models

Topic B: Build Hierarchical Clustering Models

 

Lesson 8: Building Decision Trees and Random Forests

Topic A: Build Decision Tree Models

Topic B: Build Random Forest Models

 

Lesson 9: Building Support-Vector Machines

Topic A: Build SVM Models for Classification

Topic B: Build SVM Models for Regression

 

Lesson 10: Building Artificial Neural Networks

Topic A: Build Multi-Layer Perceptrons (MLP)

Topic B: Build Convolutional Neural Networks (CNN)

Topic C: Build Recurrent Neural Networks (RNN)

 

Lesson 11: Operationalizing Machine Learning Models

Topic A: Deploy Machine Learning Models

Topic B: Automate the Machine Learning Process with MLOps

Topic C: Integrate Models into Machine Learning Systems

 

Lesson 12: Maintaining Machine Learning Operations

Topic A: Secure Machine Learning Pipelines

Topic B: Maintain Models in Production

*Important Note : Fees are subject to Singapore's prevailing Goods and Services Tax (GST).
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