Course Details
Course Details
What You'll Learn
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
Assessment
- Written Exam
- Practical Exam
Course Info
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 will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
- PyTorch – free
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2020505815.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- 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).
Trainers
Trainers
Dr. Alfred Ang approaches deep learning as an electrical engineer and researcher before he approaches it as a programmer. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, where he topped his Physics cohort, and he later completed a Graduate Diploma in Intelligent Robotics Systems at NUS-ISS. He has published more than 20 peer-reviewed papers, co-invented more than 20 US patents and worked in the defence, electronics and semiconductor industries, giving him a strong grounding in signals, hardware and real-world data. Today he is a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer, and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, having founded Tertiary Courses. His AI certifications include Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and AWS Certified AI Practitioner, and he is an IBM certified instructor for the AI Practitioners course. Applied machine learning runs through his public GitHub portfolio: his native document scanner apps for iOS and Android perform on-device OCR, with ML Kit edge detection on Android, while his passport photo apps carry out on-device background removal and face-aware cropping. He also maintains interactive Python and Streamlit labs for beginner data mining and machine learning. As an ACTA-certified trainer and DACE-certified curriculum developer, he designs his WSQ programmes so that rigorous technical content is translated into practical, industry-aligned learning experiences for working professionals.
In WSQ - Building Advanced Machine Learning and AI Solutions with Pytorch, Dr. Ang leads learners from the deep learning landscape through to working models. The course opens with the main application domains of deep learning and a discussion of AI ethics, then moves into machine learning fundamentals and neural networks, where learners use PyTorch to build regression and classification models. Recurrent neural networks follow, with hands-on work on temporal sequence modelling and sentiment analysis. Learners then build convolutional neural networks for image classification and practise techniques to resolve overfitting. The final topic applies machine learning to signal processing, covering digital signal processing, classification of signals and evaluation of performance, an area that connects directly to his doctoral training and industry background in electrical engineering. He explains the mathematics behind tensors, gradients and loss functions in plain terms, then lets learners see each idea at work in code, so that PyTorch becomes a tool they understand rather than a black box. His style is patient and practical: short explanations, live coding and guided labs, with time to experiment with architectures and hyperparameters. He also prepares learners well for the WSQ assessment by linking each activity to the skills being assessed. Participants leave able to design, train and evaluate their own deep learning models in PyTorch. He also discusses how to move a trained model beyond the notebook into a practical application. To see his machine learning and applied AI projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Yeo Hwee Theng is a data science and AI strategist with deep expertise in machine learning engineering, predictive analytics and enterprise AI transformation. As Data & Analytics Product Lead at Amplify Health, she drives large-scale AI solution development with a focus on scalable model deployment and data architecture. In earlier roles as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, she delivered advanced AI applications spanning natural language processing (NLP), computer vision and deep learning. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).
In Building Advanced Machine Learning and AI Solutions with PyTorch, she brings her hands-on applied AI experience to guide learners through end-to-end neural network design. The course opens with the deep learning landscape, its application domains and AI ethics, then introduces the PyTorch framework through regression and classification models. Learners build recurrent neural networks for sequential data and sentiment analysis, and convolutional neural networks for image classification, with techniques to resolve overfitting, before applying machine learning to signal processing. Her emphasis on practical coding labs and deployment-minded thinking connects theory with enterprise use, so participants leave confident building, evaluating and improving deep learning models of their own.
Quah Chee Yong is an ACLP-certified trainer and data science professional specialising in AI, natural language processing 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 programmes for SAP, Temasek Polytechnic and IMDA. His expertise includes reinforcement learning, deep learning and deploying AI solutions in domains such as customer analytics and chatbot development.
In WSQ Building Advanced Machine Learning and AI Solutions with PyTorch, he helps learners apply deep learning frameworks effectively through applied case studies. Participants survey deep learning application domains and AI ethics, then use PyTorch to build regression and classification models with neural networks. They build recurrent neural networks for temporal sequences and sentiment analysis, a topic that draws on his NLP background, and convolutional neural networks for image classification, including techniques to resolve overfitting. The course closes with machine learning for signal processing. By blending technical expertise with training experience, he ensures learners gain both coding proficiency and business application insight.
Teh Siew Yee is a data analytics and digital transformation leader with more than two decades of experience in the technology, finance and manufacturing sectors. Siew Yee has held leadership roles at Standard Chartered, Hewlett-Packard, TikTok and SIA Engineering, spearheading 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 ACLP-certified, blending technical acumen with instructional skill to train professionals in advanced analytics and AI adoption.
In Building Advanced Machine Learning and AI Solutions with PyTorch, Siew Yee helps participants move from machine learning concepts to working deep learning models. Learners survey the AI and deep learning landscape, key application domains and AI ethics, then learn the fundamentals of neural networks and the PyTorch framework by building regression and classification models. They go on to build recurrent neural networks for temporal sequence modelling and sentiment analysis, and convolutional neural networks for image classification, including techniques to reduce overfitting. By combining theory with real-world case applications, Siew Yee equips learners to train, evaluate and refine PyTorch models that meet industry needs and support data-driven decision-making.
Dr Alvin Ang is an ACLP-certified trainer with a PhD in Operations Research from Nanyang Technological University and over a decade of academic and industry experience in data science, AI and optimisation. He has taught at NTU, SUSS, Curtin University and SP Jain School of Global Management, and has served as an IBM Data Science Instructor. With certifications in TensorFlow, PyTorch, NLP and deep learning, he brings both research depth and practical application into his teaching.
In WSQ Building Advanced Machine Learning and AI Solutions with PyTorch, Dr Ang emphasises hands-on learning in building advanced machine learning and AI models. Learners survey deep learning application domains and AI ethics, then use the PyTorch framework to build regression and classification models with neural networks. They go on to recurrent neural networks for sequential data and sentiment analysis, convolutional neural networks for image classification with techniques to reduce overfitting, and machine learning for signal processing. His sessions guide learners through deep learning workflows and applied projects, combining academic rigour with case-based teaching. Participants leave with the theoretical knowledge and practical skills to design and implement AI solutions with real-world impact.
Review
Customer Reviews (4)
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. (Posted on 3/5/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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Would be nice if students have access to Google Colab Pro for smoother practical sessions. Current lessons operate on Colab Free Plan which limits hardware resources, slowing down processing of Python scripts on the online Jupyter Notebook platform in Colab. (Posted on 3/5/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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Great that you provide laptop! Thank God (Posted on 3/5/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 7/18/2021)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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