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Course Details
Course Details
What You'll Learn
Topic 1 Overview of Machine Learning Methodology
Introduction to Machine Learning
Machine Learning vs Deep Learning
Supervised vs Unsupervised Learning
Machine Learning Implementation Steps
Target and Features
Model Training and Prediction
Metrics to Evaluate Machine Learning Models
Topic 2 Supervised Learning Models and Applications
The Linear Regression Model
Logistics Regression Model
Naïve Bayes Model
Decision Tree Model
Random Forest Model
XGBoost Model
Neural Network Model
Topic 3 Unsupervised Learning Models and Applications
K-Means Clustering Model
Hierarchical Clustering Model
Principal Component Analysis
Assessment
- Written Exam
- Practical Exam
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Basic Python Programming Knowledge
- 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.
Target Age Group: 21-65 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
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.
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-2023018794.
- 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 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
- Data Analyst
- Research Scientist
- Business Intelligence Specialist
- Data Engineer
- Software Developer (interested in ML)
- Statistician
- Predictive Modeler
- AI Solutions Architect
- Quantitative Researcher
- Data Visualization Specialist
- Analytics Consultant
- Product Manager (focused on AI/ML products)
- Innovation Specialist
Trainers
Trainers
Dr. Alvin Ang is a data science and AI expert with a PhD in Operations Research from Nanyang Technological University, specializing in optimization, predictive modeling, and financial analytics. He has taught extensively at institutions such as SUSS, Curtin University, and SP Jain School of Global Management, covering subjects including machine learning, R, Python for finance, and quantitative methods. Professionally, he has served as a data science trainer with IBM and Tertiary Infotech, delivering courses in AI, big data, and applied machine learning.
With multiple IBM and Kaggle certifications in Python, R, data science, and deep learning, Dr. Ang combines technical mastery with applied research experience. His training approach is highly practical, guiding learners through the process of building, testing, and evaluating machine learning models for trading applications. By focusing on real-world case studies, financial datasets, and algorithmic trading workflows, he equips participants with the skills to apply machine learning techniques effectively in the financial services industry.
Teh Siew Yee is an experienced adult educator and corporate trainer specializing in business communication, workplace effectiveness, and professional development. With years of experience across both corporate and training environments, she has helped learners enhance their skills in problem-solving, collaboration, and interpersonal communication. Her ability to translate complex concepts into clear, practical strategies ensures that participants can immediately apply their learning in the workplace.
As an ACLP-certified trainer, Siew Yee delivers WSQ courses with a strong focus on learner engagement and workplace application. She integrates case studies, role-plays, and reflective exercises into her sessions, ensuring participants develop not only knowledge but also confidence in real-world contexts. By combining her corporate experience with adult education expertise, she empowers learners to improve workplace efficiency, strengthen teamwork, and achieve personal and organizational success.
Tan Woei Ming is an accomplished data scientist and AI engineer with over 15 years of experience in artificial intelligence, machine learning, and data-driven innovation. Holding a Master’s in Intelligent Systems from the National University of Singapore (NUS) and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led AI initiatives in predictive analytics, automation, and process optimization across the semiconductor and manufacturing industries. His expertise lies in translating complex AI technologies into practical business applications, enabling organizations to innovate through data insights and intelligent automation.
Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.
Dr. Alfred Ang is a specialist in artificial intelligence and data science with extensive experience applying machine learning models to real-world business and financial contexts. As Founder of Tertiary Courses Singapore, he has designed and delivered numerous WSQ and IBF-accredited programs in Python programming, data analytics, and AI adoption for financial services. His professional expertise covers areas such as predictive analytics, algorithmic modeling, and natural language processing, giving learners a strong foundation in understanding how AI transforms financial trading strategies.
With a PhD in Computer Science, Dr. Ang blends academic rigor with industry insights, helping participants bridge the gap between theory and application. He emphasizes practical, hands-on learning using financial datasets, enabling learners to build trading models that leverage supervised and unsupervised machine learning techniques. By combining deep technical knowledge with a learner-centric teaching style, Dr. Ang empowers financial professionals to adopt AI and machine learning for smarter, data-driven trading decisions.
Review
Customer Reviews (30)
- Average Rating: 4.0/5 Review by Course Participant/Trainee
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N/A (Posted on 3/13/2026)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 - Average Rating: 4.3/5 Review by Course Participant/Trainee
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Dr Alvin explains concepts really well, in quite an unorthodox way. (Posted on 3/13/2026)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 - Average Rating: 5.0/5 Review by Course Participant/Trainee
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Interesting lesson as a python newbie! (Posted on 3/13/2026)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 - Average Rating: 3.7/5 Review by Course Participant/Trainee
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N/A (Posted on 3/13/2026)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 - Average Rating: 5.0/5 Review by Course Participant/Trainee
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Dr. Alvin Ang’s Machine Learning 101 for Financial Trading is a highly accessible and informative course for those interested in applying machine learning (ML) to finance. It covers foundational ML concepts like supervised and unsupervised learning, regression, and classification, all while linking them to practical trading scenarios. Dr. Ang does an excellent job of breaking down complex topics and contextualizing them in real-world trading applications, such as stock prediction, risk management, and algorithmic trading. The course is well-structured, starting with basics and progressing to more complex algorithms like decision trees and neural networks. The hands-on approach, using Python code and practical exercises, makes the content engaging and applicable. Dr. Ang’s clear teaching style and real-world examples make the learning experience enjoyable. While the course is beginner-friendly, a deeper exploration into advanced topics (e.g., deep learning for trading) could further enhance it for those with some prior knowledge. Additionally, more coverage on accessing financial data could help learners who are new to finance. Overall, it’s a great starting point for anyone looking to combine machine learning with financial trading, offering both theory and practical skills. (Posted on 3/13/2026)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
Write Your Own Review
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