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WSQ - AI Vibe Coding for Deep Learning

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.

TSC Title: Analytics and Computational Modelling
TSC Code: ICT-DIT-3001-1.1

Funding for this course is valid from 15 Nov 2019 to 14 Nov 2027. Register and complete the course within this period to qualify for funding support.

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

LO1: Setup Deep Learning frameworks.
LO2: Understand and code Neural Network models for Regression.
LO3: Understand and code Neural Network models for Classification.
LO4: Understand and code Convolutional Neural Network models for Image Classification.
LO5: Understand and use pre-trained models for transfer learning.

Download Course Brochure

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.
OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - AI Vibe Coding for Deep Learning

Course Code: TGS-2019504744

WSQ
SFC
PSEA
SFEC
Absentee Payroll
MCES

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This course equips participants with practical skills to use AI vibe coding and Python to develop deep learning applications. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and improve code, making neural network development more accessible without requiring every component to be written manually.

Participants will explore the foundations of deep learning, including artificial neurons, network layers, activation functions, loss functions, gradient descent, and backpropagation. They will learn how to prepare datasets, design neural network architectures, train models, evaluate results, and adjust parameters to improve performance.

The course covers practical deep learning applications such as image classification, visual recognition, text analysis, and predictive modelling. Learners will build fully connected neural networks and convolutional neural networks while using AI-assisted workflows to select suitable architectures, troubleshoot training issues, and interpret model outputs.

Through hands-on projects, participants will develop end-to-end deep learning workflows, from data preparation and model development to testing, visualisation, and deployment planning. Emphasis is placed on validating AI-generated code, reducing overfitting, maintaining data quality, comparing model performance, and applying responsible AI practices.

By the end of the course, learners will be able to use AI vibe coding with Python to build, train, evaluate, and optimise deep learning models for real-world applications. This course is suitable for beginner and intermediate learners with basic programming or data analytics knowledge.

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

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

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 Form Submit PSEA Form

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

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.

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

Topic 1: AI Vibe Coding and Deep Learning Environment Setup

Topic 2: Neural Networks for Regression and Predictive Modelling

Topic 3: Neural Networks for Data Classification

Topic 4: Convolutional Neural Networks for Image Classification

Topic 5: Transfer Learning and Fine-Tuning Pre-Trained Model

Written Exam
Practical Exam
Oral Questioning

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
An AI coding assistant with a free tier, e.g. GitHub Copilot Free in Visual Studio Code

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).

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-2019504744.
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 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

AI Developer
Machine Learning Engineer
Data Scientist
Data Analyst
Python Developer
Software Developer
Computer Vision Engineer
AI Solutions Architect
Predictive Analytics Specialist
Business Intelligence Analyst
AI Product Manager
Automation Engineer
Data Engineer
Technical Consultant (AI)
AI/ML Educator or Trainer

Trainers

Deep learning sits at the centre of Dr. Alfred Ang's technical interests, alongside machine learning, robotics and programming. He is an IBM certified instructor for the AI Practitioners course, completed a Graduate Diploma in Intelligent Robotics Systems at NUS-ISS in 2021, and holds Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102) as well as AWS Certified Solutions Architect and AI Practitioner. His academic foundation is 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, and he has published more than 20 peer-reviewed papers and co-invented more than 20 US patents. He also holds a Certificate in AI Ethics and Governance (Senior Professional) from NTU, which informs how he discusses bias in training data. As founder of Tertiary Courses and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, he works as a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer. Several of his published apps put on-device vision models to practical use: scannerapp-android combines ML Kit edge detection with on-device OCR, passportphotoapp uses on-device background removal and face-aware cropping to produce compliant passport photos, and tapcardapp_android reads paper business cards with on-device OCR. For teaching, his data-mining-ml-labs repository offers interactive Python and Streamlit labs for beginners in data mining and machine learning. He works with AI coding assistants such as Claude Code, for which he has published his own skills, which is exactly the workflow this course teaches.

In WSQ - AI Vibe Coding for Deep Learning, Dr. Ang shows learners how to build neural networks in Python by describing what they want to an AI coding assistant and then testing, explaining and improving the generated code. The course starts with setting up the deep learning environment and the vibe coding workflow, then introduces the core ideas of artificial neurons, layers, activation functions, loss functions, gradient descent and backpropagation through practical builds rather than abstract derivations. Learners apply neural networks to regression and predictive modelling, then to data classification, before moving to convolutional neural networks for image classification. The final topic covers transfer learning and fine-tuning pre-trained models, the approach most real projects use because it achieves strong results with modest data and compute. His physics and engineering background helps him explain why a model is underfitting or overfitting and what a learning curve is really saying, so learners can direct the AI assistant with precise instructions instead of guessing. He insists that learners read and question the code the assistant produces, checking data splits, metrics and tensor shapes before trusting a result. The sessions are hands-on throughout, with each topic ending in a working notebook that learners can extend on their own data. As an ACTA-certified trainer and DACE-certified curriculum developer, he paces the material so that learners without a deep mathematics background can still build and evaluate models with confidence. To explore his AI and machine learning projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Quah Chee Yong is an ACLP-certified trainer with strong expertise in data science, machine learning and natural language processing. He has served as Data Science Training Lead at MSITEK and, previously, as AI Solutions Lead at AiDeal Scan, where he developed predictive analytics, recommender systems and NLP-driven applications using frameworks such as TensorFlow and Keras. He has designed and delivered AI training programmes for SAP, Temasek Polytechnic and IMDA, equipping learners from technical and non-technical backgrounds with essential machine learning skills.

In WSQ AI Vibe Coding for Deep Learning, he guides learners through data preparation, network design and model evaluation using AI coding assistants. Participants set up a deep learning environment, then build neural networks for regression and predictive modelling, neural networks for data classification and convolutional neural networks for image classification, before applying transfer learning and fine-tuning to pre-trained models. His sessions are project-based: learners describe what they want in natural language, validate the generated code and interpret the results. His accessible explanations help participants gain both confidence and competence in developing deep learning models.

Solomon Soh is a data scientist and AI trainer who has applied machine learning and deep learning to natural language processing, computer vision and optimisation. At IBM Singapore he supervised numerous machine learning and deep learning projects, coaching teams on model design, data preprocessing and deployment. His career includes data science roles at Workforce Optimizer and Certis Cisco, where he implemented forecasting, reinforcement learning and predictive analytics solutions using TensorFlow and scikit-learn. Certified in AI engineering and machine learning, he has served as lead instructor for data science bootcamps and corporate training programmes.

In WSQ - AI Vibe Coding for Deep Learning, Solomon guides learners through AI-assisted deep learning workflows, helping them build, train and evaluate neural network and CNN models. Participants set up an AI vibe coding and deep learning environment, use neural networks for regression and predictive modelling and for data classification, apply convolutional neural networks to image classification, and use transfer learning to fine-tune pre-trained models. His teaching emphasises hands-on vibe coding, good practice in model development and real-world case applications, so learners can generate, test and debug code they understand and apply deep learning with confidence.

Dr Alvin Ang holds a PhD in Operations Research from Nanyang Technological University and brings extensive academic and industry expertise in machine learning, AI and optimisation. He has taught at NTU, SUSS, Curtin University and SP Jain School of Global Management, as well as in professional training roles with IBM and Tertiary Infotech, giving him more than a decade of teaching experience. An ACLP-certified trainer, he has earned multiple IBM certifications in machine learning, deep learning and TensorFlow, which he integrates into his course delivery.

In WSQ AI Vibe Coding for Deep Learning, Dr Ang applies this deep learning background to an AI-assisted way of building models in Python. Learners set up their AI vibe coding and deep learning environment, then build neural networks for regression and predictive modelling, neural networks for data classification, and convolutional neural networks for image classification, before moving on to transfer learning and fine-tuning pre-trained models. His structured approach starts with neural network fundamentals so that learners understand the architecture behind the code their assistants generate. Blending theory with practical AI-assisted coding exercises, he ensures participants leave able to design, evaluate and optimise their own deep learning models.

Terence Ee is an independent trainer and consultant who, since 2017, has helped professionals upskill in Python, data analytics and machine learning. He combines this with more than 25 years of experience in technology management, systems integration and digital transformation, including senior roles as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he oversaw enterprise-level IT strategy and system development. With academic qualifications in computer science and technology management, he pairs executive insight with technical expertise in software development and emerging technologies. He is also an ACLP-certified, WSQ-accredited trainer.

Terence's approach to AI vibe coding starts with a solid foundation in Python, then guides learners to prompt, validate and refine AI-generated deep learning code rather than accept it blindly. In this course, participants set up their deep learning environment, build neural networks for regression, predictive modelling and data classification, and progress to convolutional neural networks for image classification. The final topic on transfer learning and fine-tuning pre-trained models shows how to achieve strong results without training from scratch. Combining his industry background with hands-on teaching, he ensures learners gain the applied knowledge to implement deep learning confidently in real organisational contexts.