WSQ - Building Advanced Machine Learning and AI Solutions with Pytorch

Topic 1: Overview of Deep Learning Application Domains

• AI and Deep Learning Landscape
• Deep Learning Application Domains
• AI Ethnics

Topic 2: Machine Learning for Regression and Classification

• General Concepts of Machine Learning and Neural Network (NN)
• Introduction to Pytorch Framework
• Build a Regression Model Using NN
• Build a Classification Model Using NN

Topic 3: Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• Build a Temporal Sequential Model Using RNN
• Build a Sentimental Analysis Model Using RNN

Topic 4: Convolutional Neural Network (CNN)

• Introduction to Convolutional Neural Network (CNN)
• Build a Image Classification Model Using CNN
• Techniques to Resolve Overfitting Issue

Topic 5: Application of Machine Learning to Signal Processing

• Digital Signal Processing
• Machine Learning Classification for Signal Processing
• Evaluation of Signal Processing Performance

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

• Positive Learning Attitude
• Enthusiastic Learner

Experience

• Minimum of 1 year of working experience.
• Minimum 18 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

• PyTorch

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• Data Scientist
• Machine Learning Engineer
• Statistician
• R Developer
• Quantitative Researcher
• Bioinformatics Scientist
• Predictive Modeler
• Data Analyst (focusing on advanced analytics)
• Big Data Specialist (using R)
• Algorithm Developer
• AI Researcher (using R)
• Financial Quantitative Analyst
• Business Intelligence Specialist (with R expertise)
• Marketing Analytics Specialist (using R)
• Epidemiologist (utilizing machine learning).

Yeo Hwee Theng: Yeo Hwee Theng is a data science and AI strategist with deep expertise in machine learning engineering, predictive analytics, and enterprise AI transformation. As the Data & Analytics Product Lead at Amplify Health, she drives large-scale AI solution development across healthcare and enterprise ecosystems, focusing on scalable model deployment and data architecture. With prior experience as an AI & Data Architect at Huawei International and a Senior Data Scientist at DataRobot, she has delivered advanced AI applications spanning natural language processing (NLP), computer vision, and deep learning. Hwee Theng holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore (NUS) and an Advanced Certificate in Learning and Performance (ACLP).

In “Building Advanced Machine Learning and AI Solutions with PyTorch,” Hwee Theng brings her hands-on experience in applied AI development to guide learners through end-to-end model design and implementation using the PyTorch framework. Her sessions emphasize neural network construction, optimization techniques, and real-world deployment strategies for AI systems. Through practical coding labs and case studies, she enables participants to build and scale deep learning solutions with confidence bridging theory with enterprise-ready application.

Teh Siew Yee: Teh Siew Yee is a seasoned data analytics and digital transformation leader with over 20 years of experience in technology, finance, and manufacturing sectors. He has held leadership roles at organizations such as Standard Chartered, Hewlett-Packard, TikTok, and SIA Engineering, where he spearheaded initiatives in data governance, machine learning, and business intelligence. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and is certified in ACLP, blending technical acumen with instructional excellence to train professionals in advanced analytics and AI adoption.

In “Building Advanced Machine Learning and AI Solutions with PyTorch,” Siew Yee focuses on helping participants master the practical implementation of AI models using the PyTorch framework. His sessions cover model training, fine-tuning, and evaluation for applications such as image recognition and predictive modeling. By integrating theoretical depth with real-world case applications, he empowers learners to build efficient, high-performing AI systems tailored to industry needs, fostering innovation and data-driven decision-making.

Dr Alvin Ang – Dr Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and over a decade of academic and industry experience in data science, AI, and optimization. He has taught at NTU, SUSS, Curtin University, and SP Jain School of Global Management, and also served as an IBM Data Science Instructor. With certifications in TensorFlow, PyTorch, NLP, and deep learning, Dr Ang brings both research depth and practical application into his teaching.

In his PyTorch training, Dr Ang emphasizes hands-on learning in building advanced machine learning and AI models. His sessions guide learners through neural networks, deep learning workflows, and applied projects, ensuring they gain both theoretical knowledge and practical skills. By combining academic rigor with case-based teaching, he empowers participants to design and implement AI solutions with real-world impact.

Quah Chee Yong – Quah Chee Yong is an ACLP-certified trainer and data science professional specializing in AI, NLP, and machine learning. He has served as AI Solutions Lead at AiDeal Scan, developing recommender systems and NLP-powered search engines, and as Data Science Training Lead at MSITEK, delivering AI programs for SAP, Temasek Polytechnic, and IMDA. His expertise includes reinforcement learning, deep learning, and deploying AI solutions across domains such as customer analytics and chatbot development.

In his PyTorch courses, Quah focuses on helping learners understand and apply deep learning frameworks effectively. His training covers advanced neural networks, natural language processing, and recommender systems, all taught through applied case studies. By blending technical expertise with training experience, Quah ensures learners gain both the coding proficiency and business application insights needed to build advanced AI solutions.

Terence Ee – Terence Ee is an independent consultant and ACLP-certified trainer with more than 25 years of experience in IT management, enterprise systems, and digital transformation. He has held leadership roles such as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he drove enterprise-level technology adoption. Holding a B.Sc. in Computer Science from NUS and an M.Sc. in Technology Management from Staffordshire University, Terence combines executive insights with technical training.

In his machine learning and AI training, Terence focuses on bridging strategy with technical application. His PyTorch sessions introduce learners to advanced ML workflows and deep learning frameworks, contextualized within real-world business use cases. By integrating his leadership background with applied technical training, Terence ensures participants can apply AI solutions not just technically, but strategically within their organizations.