WSQ - Basic Machine Learning with ScikitLearn Course

Topic 1: Overview of Machine Learning and Scikit Learn

• Introduction to Machine Learning
• Supervised vs Unsupervised Learnings
• Machine Learning Applications and Case Studies
• What is Scikit Learn
• Installing Scikit-Learn

Topic 2: Classification

• What is Classification
• Applications of Classification
• Classification Algorithms
• Classification Workflow
• Confusion Matrix
• Classification Performance Evaluation

Topic 3: Regression

• What is Regression
• Applications of Regression
• Regression Algorithms
• Regression Workflow
• Regression Performance Evaluation

Topic 4: Clustering

• What is Clustering
• Applications of Clustering
• Clustering Algorithms
• Clustering Workflow
• Clustering Performance Evaluation

Topic 5: Principal Component Analysis

• Introduction to Principal Component Analysis (PCA)
• Application of PCA
• PCA Workflow

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Case Study (CS)
• Oral Questioning (OQ)

Promotion Code

Promo or discount cannot be applied to WSQ courses

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 can download and install the following software:

• Scikit-Learn Course

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• 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

Quah Chee Yong - Quah Chee Yong is an ACLP-certified adult educator with strong expertise in data science, machine learning, and natural language processing. He has led data training initiatives with SAP, Temasek Polytechnic, and IMDA, delivering AI and machine learning courses to both technical and non-technical learners. With professional experience as Head of Data Science at GoWild Singapore and AI Solutions Lead at AiDeal Scan, he has built recommender systems, NLP solutions, and predictive models, applying scikit-learn, TensorFlow, and other frameworks to solve business challenges.

Quah has conducted numerous machine learning workshops, including projects in classification, regression, and clustering. His training emphasizes hands-on practice with Python and scikit-learn, ensuring participants develop a strong foundation in model building, evaluation, and deployment. With his ability to simplify technical concepts and link them to practical business cases, he equips learners with the applied skills to confidently leverage machine learning for real-world problem-solving.

Dr Alvin Ang - Dr Alvin Ang holds a Ph.D. in Operations Research from NTU and has more than a decade of teaching and research experience in data science and machine learning. As a data science and AI trainer, he has taught courses at institutions such as NTU, SUSS, and Curtin University, as well as in professional settings with IBM and Tertiary Infotech. His expertise covers Python, scikit-learn, machine learning, and applied statistics, supported by numerous IBM certifications in data science and machine learning.

An ACLP-certified trainer, Dr Ang has trained professionals in Python programming, machine learning model development, and AI applications across industries. He emphasizes practical learning with scikit-learn, guiding learners through data preprocessing, model selection, hyperparameter tuning, and performance evaluation. His engaging and structured approach ensures learners gain both theoretical knowledge and the confidence to implement machine learning in real-world projects.

Solomon Soh - Solomon Soh is an accomplished data scientist and AI trainer with extensive experience in applying machine learning to natural language processing, computer vision, and optimization problems. At IBM Singapore, he supervised 24 machine and deep learning projects, coaching teams in feature engineering, data cleansing, and ensemble modeling. His work at Workforce Optimizer and Certis Cisco focused on predictive analytics, reinforcement learning, and operational optimization, where he successfully applied scikit-learn and other frameworks to real-world problems.

As a certified AI engineer and experienced trainer, Solomon has taught data science bootcamps and corporate workshops covering Python, scikit-learn, and advanced machine learning methods. He emphasizes project-based learning, helping participants gain practical experience in model development, from regression and classification to clustering and optimization. His teaching style blends technical rigor with clear communication, ensuring learners can effectively apply machine learning techniques to diverse business and research challenges.

Terence Ee - Terence Ee is a seasoned IT leader and independent trainer with over 25 years of experience in technology management, systems development, and enterprise IT solutions.He has held senior positions such as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he oversaw complex digital transformation projects. With academic qualifications in computer science and technology management, Terence brings a wealth of industry insight into applying advanced computing solutions.

Since 2017, Terence has focused on training and consulting, helping professionals develop skills in software development, coding best practices, and emerging technologies. His teaching in Python and machine learning introduces learners to scikit-learn through practical exercises in model building, evaluation, and application to business contexts. By bridging his leadership experience in IT with hands-on technical training, Terence equips learners with the knowledge to apply machine learning effectively in organizational settings.

Truman Ng - Truman Ng is an ACTA-certified trainer and PMP-certified project manager with extensive experience in IT, networking, and applied data science. He has conducted training in machine learning, Python programming, and cybersecurity, with a focus on practical applications. His expertise includes using scikit-learn, TensorFlow, and related frameworks to teach supervised and unsupervised learning, covering areas such as classification, clustering, and regression.

With a strong background in enterprise systems and certifications across Cisco, Huawei, and cloud platforms, Truman blends technical depth with practical training delivery. He has trained professionals in applying scikit-learn for machine learning model development, ensuring they understand not only the algorithms but also data preparation, feature engineering, and deployment. His structured and hands-on teaching style empowers learners to confidently apply machine learning to solve business and technical challenges