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WSQ - AI Vibe Coding for Machine 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 Oct 2019 to 14 Oct 2027. Register and complete the course within this period to qualify for funding support.

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

LO1: understand and apply machine learning concepts
LO2: understand and apply classification methods
LO3: understand and apply regression methods
LO4: understand and apply clustering methods
LO5: understand and apply PCA methods

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 Machine Learning

Course Code: TGS-2019504643

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 build machine learning solutions using scikit-learn. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and refine Python code, making machine learning development faster and more accessible.

Participants will learn how to prepare data for machine learning by importing, cleaning, transforming, encoding, and organising datasets. They will use scikit-learn to develop machine learning workflows covering data preprocessing, feature selection, model training, prediction, and evaluation.

The course covers key supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Learners will explore commonly used algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbours, K-Means Clustering, and Principal Component Analysis (PCA).

Using AI-assisted workflows, learners will generate and refine scikit-learn code, select suitable algorithms, troubleshoot errors, and compare model performance. They will apply train-test splitting, cross-validation, feature scaling, hyperparameter tuning, and scikit-learn Pipelines to improve model reliability.

Through hands-on projects, participants will build end-to-end machine learning solutions, from preparing data and training models to evaluating, improving, and interpreting results. Emphasis is placed on validating AI-generated code, preventing overfitting and data leakage, and selecting appropriate evaluation metrics.

By the end of the course, learners will be able to use AI vibe coding, Python, and scikit-learn to develop and evaluate practical machine learning solutions.

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 Machine Learning Fundamentals

Topic 2: Data Classification and Performance Evaluation

Topic 3: Regression Analysis and Predictive Modelling

Topic 4: Clustering and Customer or Data Segmentation

Topic 5: Principal Component Analysis and Dimensionality Reduction

Written Exam
Case Study
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-2019504643.
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

Machine Learning Engineer
Data Scientist
Data Analyst
AI Engineer
Business Analyst
Data Engineer
Software Developer moving into machine learning
Research Analyst
Predictive Modelling Specialist
Analytics Consultant
Product Manager (focused on data or AI products)
Innovation Specialist
Business Owner or Manager working with data

Trainers

Dr. Alfred Ang has worked with machine learning as a researcher, a certified practitioner and a developer who now writes much of his code alongside AI assistants. 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, and he has published more than 20 peer-reviewed papers and co-invented more than 20 US patents after working in the defence, electronics and semiconductor industries. He went on to complete a Graduate Diploma in Intelligent Robotics Systems at NUS-ISS, and his certifications include Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102), AWS Certified AI Practitioner, and IBM certified instructor status for the AI Practitioners course. As a full-stack AI developer and Agentic AI solution architect, he is Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd and founder of Tertiary Courses. His GitHub, with more than 300 public repositories, reflects both strands of this course: data-mining-ml-labs is a set of interactive Python and Streamlit labs for beginner data mining and machine learning, while skills is a published collection of Claude Code skills, and wsq-casl-cp-generator is a Streamlit web app powered by Claude Code content generation. He works with AI coding assistants as a matter of routine, which is exactly the workflow this WSQ course teaches.

In WSQ - AI Vibe Coding for Machine Learning, Dr. Ang shows learners how to describe what they want in natural language, let an AI coding assistant generate the Python, and then read, test and improve that code until they understand every line. The course begins with AI vibe coding and machine learning fundamentals, including importing, cleaning, transforming and encoding data so that scikit-learn can use it. Learners then tackle data classification and performance evaluation, training classifiers and judging them with confusion matrices, accuracy, precision and recall rather than a single headline number. Regression analysis and predictive modelling follow, then clustering for customer or data segmentation, and finally principal component analysis for dimensionality reduction and visualisation. His research background means he explains why each algorithm works, not just how to call it, and he is quick to point out when AI-generated code produces a plausible but wrong answer, such as leaking test data into training or misreading a metric. Every topic ends with a working notebook or small application that learners have built and debugged themselves, in the same spirit as his interactive machine learning labs. As an ACTA-certified trainer and DACE-certified curriculum developer, he paces the course for professionals with little programming experience, so they leave able to prototype real machine learning solutions with AI as a capable but closely supervised assistant. Learners also practise writing clear prompts that specify data, features and evaluation metrics so the AI produces code worth keeping. To see his machine learning and AI coding 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 adult educator with strong expertise in data science, machine learning and natural language processing. He has served as Head of Data Science at GoWild Singapore and as AI Solutions Lead at AiDeal Scan, building recommender systems, NLP solutions and predictive models with scikit-learn, TensorFlow and other frameworks. He has led data training initiatives with SAP, Temasek Polytechnic and IMDA, delivering AI and machine learning courses to technical and non-technical learners, and has run numerous machine learning workshops on classification, regression and clustering.

WSQ AI Vibe Coding for Machine Learning plays directly to these strengths. He guides learners in using natural-language instructions and AI coding assistants to generate, explain, test and debug scikit-learn code in Python. Participants cover machine learning fundamentals, data classification and performance evaluation, regression analysis and predictive modelling, clustering for customer or data segmentation, and principal component analysis for dimensionality reduction. His ability to simplify technical concepts and link them to practical business cases helps learners build a strong foundation in data preparation, analysis and visualisation, and apply machine learning confidently to real-world problems.

Dr Alvin Ang is a data science and AI trainer with a PhD in Operations Research from NTU and more than a decade of teaching and research experience in data science and machine learning. His expertise spans Python, scikit-learn, machine learning and applied statistics, backed by numerous IBM certifications in data science and machine learning. He has taught at NTU, SUSS and Curtin University, as well as in professional settings with IBM and Tertiary Infotech, and as an ACLP-certified trainer he has trained professionals across industries in Python programming, machine learning model development and AI applications.

WSQ AI Vibe Coding for Machine Learning matches his scikit-learn and applied statistics strengths closely. Learners use AI coding assistants and natural-language instructions to work through machine learning fundamentals, data classification and performance evaluation, regression analysis and predictive modelling, clustering for customer or data segmentation, and principal component analysis. Dr Ang emphasises the steps that make models trustworthy, from data preprocessing and exploratory analysis to visualisation and careful interpretation of results. His structured approach ensures participants understand what the generated code is doing, so they finish the course able to build, evaluate and explain machine learning models for real-world projects.

Solomon Soh is a data scientist and AI trainer with extensive experience applying machine learning to natural language processing, computer vision and optimisation problems. At IBM Singapore he supervised numerous machine learning and deep learning projects, coaching teams in feature engineering, data cleansing and ensemble modelling. His work at Workforce Optimizer and Certis Cisco focused on predictive analytics, reinforcement learning and operational optimisation, where he applied scikit-learn and other frameworks to real-world problems. A certified AI engineer, he has taught data science bootcamps and corporate workshops covering Python, AI-assisted coding and applied data analytics.

In WSQ - AI Vibe Coding for Machine Learning, Solomon helps learners use natural-language instructions and AI coding assistants to build machine learning solutions with Python and scikit-learn. Participants begin with AI vibe coding and machine learning fundamentals, then work through data classification and performance evaluation, regression analysis and predictive modelling, clustering for customer and data segmentation, and principal component analysis for dimensionality reduction. His project-based approach gives learners practical experience across the model development process. He blends technical rigour with clear communication, so participants can apply these techniques to business and research challenges with confidence.

Truman Ng is an ACTA-certified trainer with extensive experience in applied data science, IT and networking. He has conducted training in machine learning, Python programming and cybersecurity, with a strong focus on practical application, and uses scikit-learn, TensorFlow and related frameworks to teach supervised and unsupervised learning, including classification, clustering and regression. A PMP-certified project manager, he also holds certifications across Cisco, Huawei and cloud platforms, and his strong background in enterprise systems helps him relate machine learning techniques to real business and technical problems.

In WSQ AI Vibe Coding for Machine Learning, Truman teaches participants to use natural-language prompts and AI coding assistants to generate, explain, test and refine Python code for machine learning with scikit-learn. Learners prepare data by importing, cleaning, transforming and encoding datasets, then build and evaluate classification models, carry out regression and predictive modelling, segment data with clustering, and reduce dimensionality with principal component analysis. He makes sure learners understand not only the tools but also data preparation, model evaluation and result interpretation. His structured, hands-on style helps participants apply machine learning confidently to business and technical challenges.