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AI Vibe Coding for Advanced Data Modeling

Course Information

  • Sessions 2 days
  • Duration 16 hrs
  • Level Beginner to Intermediate
  • Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Funding Validity

Funding for this course is valid from 28 Jul 2026 to 27 Jul 2028. Register and complete the course within this period to qualify for funding support.

Learning Outcomes

By end of the course, learners should be able to:

  • LO1: Apply machine learning principles to gain business insights.
  • LO2: Aggregate data to help test problem.
  • LO3: Apply predictive data modeling techniques to identify underlying trend and patterns in data using neural networks.
  • LO4: Develop prototype classification model using machine learning techniques to gain new insight from data.
  • LO5: Identify patterns using convolutional neural network model to derive insights and make decision.
  • LO6: Use data visualisation tool to create interactive visualizations of data.

Course Brochure

Download Course Brochure

Certification

  • Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

CASL - AI Vibe Coding for Advanced Data Modeling

Course Code: TGS-2026064720
  • CASL
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

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What's This Course About

AI Vibe Coding for Advanced Data Modeling equips participants with practical skills to build advanced predictive models using deep learning and AI-assisted coding techniques. Through natural-language instructions and AI coding assistants, learners will generate, explain, test, debug, and refine code for neural network models, making advanced data modelling more accessible and efficient.

Participants will begin with an overview of deep learning, exploring fundamental concepts such as tensors, neural network architectures, activation functions, loss functions, optimisers, gradient computation, and model training. They will then develop neural networks for regression to predict continuous outcomes and neural networks for classification to categorise data and support data-driven decision-making.

The course progresses to Convolutional Neural Networks (CNNs), where participants will learn to build models for image and pattern recognition tasks. Learners will also explore Transformers and self-attention, understanding how attention mechanisms enable models to identify relationships and contextual patterns within complex and sequential data.

Throughout the course, participants will use AI Vibe Coding to accelerate model development, troubleshoot errors, optimise model performance, and interpret results. By the end of the course, learners will be able to apply deep learning techniques to develop, evaluate, and refine advanced data models for prediction, classification, pattern recognition, and other real-world applications.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI vibe coding courses.

CASL Funding

Full Fee $750.00 Before GST
GST $67.50 9% of fee
Baseline Nett $442.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $292.50 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible companies can use their $10,000 SFEC to cover up to 90% of out-of-pocket course fees for classes ending on or before 30 Nov 2026. A redesigned SFEC starts on 1 Dec 2026.

View on SkillsFuture for Business

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used.

Direct Application on SkillsFuture Portal

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year.

Submit UTAP Claim

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

Check PSEA Eligibility

  • Scroll down to “Keyword Tags” to verify for PSEA eligibility.
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form, then submit the completed form to us:

PSEA Withdrawal FormSubmit PSEA Form

Course FeeBefore Funding

$750.00 (GST-exclusive)
$817.50 (GST-inclusive)

Course Date

Course Time

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Overview of Deep Learning

Topic 2 Neural Network for Regression

Topic 3 Neural Network for Classification

Topic 4 Convolutional Neural Network

Topic 5 Transformer and Self-Attention

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:

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).

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 PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.

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.
  1. Check your company's SFEC eligibility and balance on EnterpriseSG.
  2. 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.
  3. Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
  4. 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.

  1. Make sure your NTUC membership is paid up before the course starts.
  2. Register for this course with us and pay the course fee after funding.
  3. Attend at least 75% of the course and sit all assessments.
  4. 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.
  5. 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).
  6. 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.

  1. Check that this course shows the PSEA tag on its MySkillsFuture page.
  2. Check your PSEA balance on MOE's Student Finance System with Singpass.
  3. Download and complete the PSEA withdrawal form, then submit it to us.
  4. 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.
  5. 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.

  1. 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.
  2. Discuss the decision with the assessor or the assessment manager first.
  3. 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.
  4. Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
  5. 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.
  6. You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • Deep Learning Specialist
  • Predictive Modeling Specialist
  • Computer Vision Engineer
  • NLP Engineer
  • Data Analyst (expanding into deep learning)
  • Artificial Intelligence Consultant
  • Business Intelligence Specialist
  • Financial Forecasting Analyst
  • Data Engineer
  • Research Analyst
  • Analytics Consultant
  • Business Owner or Manager working with data

Trainers

Trainers

Dr. Alfred Ang brings a physicist's mathematical depth and an engineer's practicality to deep learning. He earned a B.Sc (Hons) in Physics from NUS, where he topped his cohort, followed by an MSc in Electrical and Electronic Engineering from NTU and a PhD in Electrical Engineering from NUS, and he later completed a Graduate Diploma in Intelligent Robotics Systems at NUS-ISS. He has published more than 20 peer-reviewed papers and co-invented more than 20 US patents. 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 credentials include Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102) and AWS Certified AI Practitioner, and he is an IBM certified instructor for the AI Practitioners course. His mathematical curiosity shows clearly in his public GitHub portfolio, which includes interactive explainers on Lie groups and Lie algebras, hyperbolic geometry, point-set topology and quantum computing, each built with hand-rolled canvas animations. He also maintains Python and Streamlit labs for data mining and machine learning, and he develops much of his software today with AI coding assistants, publishing his own Claude Code skills on GitHub. With experience in the defence, electronics and semiconductor industries, he understands that a model's value lies in reliable predictions on real data.

In CASL - AI Vibe Coding for Advanced Data Modeling, Dr. Ang uses AI coding assistants to make deep learning accessible without making it shallow. The course opens with an overview of deep learning, covering tensors, neural network architectures, activation and loss functions, optimisers, gradient computation and model training. Learners then build neural networks for regression to predict continuous outcomes, followed by networks for classification. The fourth topic introduces convolutional neural networks for image data, and the fifth explores transformers and self-attention, the architecture behind today's large language models. At each step learners describe what they want in natural language, ask the assistant to generate the code, and then explain, test, debug and refine it. He insists on understanding: learners should be able to say why a loss curve looks the way it does, why a model overfits or why attention weights matter, and he uses his mathematical background to make these ideas intuitive rather than intimidating. He also shows how to catch subtle errors in AI-generated code, such as wrong tensor shapes, data leakage or mismatched metrics. The class is hands-on and iterative, with guided labs that build steadily in complexity, and activities support the CASL assessment. Participants leave able to build, evaluate and improve advanced predictive models with AI as a capable coding partner. To explore his machine learning labs and mathematical explainers, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

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 modelling in natural language processing, computer vision and chatbots, and earned a 96% learner satisfaction rating. In his roles at Workforce Optimizer and Certis Cisco he implemented forecasting models, reinforcement learning and optimisation pipelines using PyTorch, TensorFlow and scikit-learn. A certified AI Engineer and Workera-certified Data Scientist, he has taught as lead instructor for bootcamps and corporate training across Asia.

CASL AI Vibe Coding for Advanced Data Modeling draws directly on his PyTorch and predictive modelling experience. Learners begin with an overview of deep learning, then build neural networks for regression and for classification, progress to convolutional neural networks, and finish with transformers and self-attention. Solomon guides learners through PyTorch-based workflows using natural-language instructions and AI coding assistants, emphasising hands-on coding and real-world applications. His industry background helps participants understand why each modelling choice matters, so they leave able to build working deep learning applications rapidly with AI-assisted coding and to evaluate them critically.

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 programmes for SAP, Temasek Polytechnic and IMDA. He previously served as Head of Data Science at GoWild Singapore and AI Solutions Lead at AiDeal Scan, building recommender systems, NLP applications and predictive models with TensorFlow, Keras and PyTorch. His hands-on expertise covers regression, classification, time series forecasting and neural networks.

In CASL AI Vibe Coding for Advanced Data Modeling, he takes a practical, project-based approach to AI-assisted deep learning. Learners use natural-language instructions and AI coding assistants to generate, explain, test and refine neural network code, starting with an overview of deep learning. They then build neural networks for regression and for classification, work with convolutional neural networks, and finish with Transformers and self-attention, the architecture behind today's large language models. His ability to bridge technical depth with business applications helps participants gain the confidence and competence to apply advanced predictive modelling to real-world challenges.

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, machine learning and computer vision. He has taught machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV, with an emphasis on building neural network models that forecast trends, detect anomalies and classify outcomes. He began programming on 8-bit computers in the late 1970s, holds an M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin-Madison, and has co-founded multiple high-tech companies and led AI-driven software development in healthcare, defence and manufacturing.

In CASL AI Vibe Coding for Advanced Data Modeling, Richard helps participants use AI coding assistants to generate, explain, test, debug and refine deep learning code. Learners start with an overview of deep learning, then build neural networks for regression and classification, develop convolutional neural networks and explore transformers and self-attention. His decades of hands-on development mean he can show learners why a model behaves as it does, not just how to produce it. His structured, beginner-friendly instruction equips participants with practical skills to build AI solutions from data.

Dr Alvin Ang is an ACLP-certified AI and data science trainer with a PhD 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 and Curtin University, and as an IBM Data Science Instructor. He is 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.

CASL AI Vibe Coding for Advanced Data Modeling draws directly on his expertise in the complete predictive analytics pipeline. Learners start with an overview of deep learning, then build neural networks for regression and for classification, progress to convolutional neural networks, and finish with transformers and self-attention. Throughout, Dr Ang shows how to implement these models with AI coding assistants for real-world use cases, combining hands-on coding with structured instruction. His focus on building, evaluating and deploying predictive models ensures participants do not simply generate code but understand how well their models perform, leaving them able to transform data into actionable insights.

Review

Customer Reviews (8)

Average Rating: 4.7/5 Review by Course Participant/Trainee
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Average Rating: 4.7/5 Review by Course Participant/Trainee
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2. Do you find the trainer knowledgeable in this subject?
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Trainer is knowledgeable and excellent. The classroom could look better. (Posted on 8/25/2026)
Average Rating: 5.0/5 Review by Course Participant/Trainee
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Good teacher (Posted on 8/25/2026)
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. (Posted on 8/26/2025)
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. (Posted on 7/2/2025)

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