WSQ - Predictive Analytics with PyTorch: Transform Your Data to Prediction

Topic 1 Overview of Deep Learning and Pytorch

• Overview of Deep Learning
• Introduction to Pytorch
• Install and Run Pytorch
• Basic Pytorch Tensor Operations
• Computation Graphs
• Compute Gradients with Autograd

Topic 2 Neural Network for Regression

• Introduction to Neural Network (NN)
• Activation Function
• Loss Function and Optimizer
• Machine Learning Methodology
• Build a NN Predictive Regression Model
• Load and Save Model

Topic 3 Neural Network for Classification

• Softmax
• Cross Entropy Loss Function
• Build a NN Classification Model

Topic 4 Convolutional Neural Network for Pattern Recognition

• Introduction to Convolutional Neural Network (CNN)
• Convolution & Pooling
• Build a CNN Model for Pattern Recognition

Topic 5 Data Visualization with Tensorboard

• Set up TensorBoard
• Inspect a model architecture using TensorBoard
• Create interactive Visualizations

Mode of 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.

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• Deep Learning Specialist
• PyTorch Developer
• Computer Vision Engineer
• NLP Engineer (using PyTorch)
• Data Analyst (expanding into deep learning)
• Artificial Intelligence Consultant
• Autonomous Systems Developer
• Financial Forecasting Analyst (using AI)
• Bioinformatics Researcher (utilizing deep learning)
• Neural Network Researcher
• Predictive Analytics Specialist
• Quantitative Modeler.

Quah Chee Yong - Quah Chee Yong is an ACLP-certified trainer with extensive experience in data science, predictive analytics, and machine learning. As Data Science Training Lead at MSITEK, he has delivered AI programs under SAP, Temasek Polytechnic, and IMDA, equipping both technical and non-technical learners with practical data skills. He previously served as Head of Data Science at GoWild Singapore and AI Solutions Lead at AiDeal Scan, where he built recommender systems, NLP applications, and predictive models using frameworks such as TensorFlow, Keras, and PyTorch.

His training emphasizes a practical, project-based approach to predictive analytics. With hands-on expertise in regression, classification, time series forecasting, and neural networks, Quah guides learners in leveraging PyTorch to design, train, and deploy predictive models. His ability to bridge technical depth with business applications ensures participants gain both confidence and competence in applying predictive analytics to real-world challenges.

Solomon Soh Zhe Hong - Solomon Soh is a data scientist and AI trainer with proven expertise in advanced predictive analytics and deep learning. At IBM Singapore, he supervised 24 machine learning and deep learning projects, including predictive modeling in NLP, computer vision, and chatbots. His professional roles at Workforce Optimizer and Certis Cisco saw him implement forecasting models, reinforcement learning, and optimization pipelines using PyTorch, TensorFlow, and scikit-learn.

As a certified AI Engineer and Workera-certified Data Scientist, Solomon has taught as a lead instructor for bootcamps and corporate training across Asia. He emphasizes hands-on coding and real-world applications, guiding learners through PyTorch-based workflows for regression, classification, and time series prediction. With his strong teaching background and industry experience, he equips participants to transform raw data into accurate and actionable predictions.

Terence Ee - Terence Ee is an independent consultant and trainer with more than 25 years of experience in IT leadership, systems integration, and digital transformation. He has held senior roles including Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he oversaw enterprise-scale IT strategies and technology projects. With a B.Sc. in Computer Science from NUS and an M.Sc. in Technology Management from Staffordshire University, he combines technical expertise with executive-level leadership.

Since 2017, Terence has focused on training in Python, data analytics, and machine learning, with a growing emphasis on predictive modeling using PyTorch. His courses help participants understand the end-to-end predictive analytics pipeline, from data preparation and feature engineering to model training and evaluation. By drawing on his industry experience, he ensures learners develop practical, business-relevant skills in predictive analytics that can be applied to real-world decision-making.

Richard Wan - Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.

Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

Dr Alvin Ang - Dr Alvin Ang is an ACLP-certified AI and data science trainer with a Ph.D. in Operations Research from Nanyang Technological University. With over a decade of experience in academia and industry, he has taught machine learning, predictive analytics, and deep learning at NTU, SUSS, Curtin University, and as an IBM Data Science Instructor. He is also the founder of DataFrens.sg, an open-source data science community, and has earned multiple IBM certifications in Python, machine learning, and deep learning using TensorFlow and PyTorch.

Dr Ang’s expertise lies in guiding learners through the complete predictive analytics pipeline. His courses cover regression models, ensemble methods, and neural networks, with a focus on implementing them in PyTorch for real-world use cases. Through hands-on coding and structured instruction, he equips participants with the ability to build, evaluate, and deploy predictive models, ensuring they leave with the skills to transform data into actionable insights.