Course Details
Course Details
What You'll Learn
Topic 1: Overview of Machine Learning
- Introduction to Machine Learning
- Pattern Recognition Problems Suitable for Machine Learning
- Supervised vs Unsupervised Learnings
- Types of Machine Learning
- Machine Learning Techniques
- R Packages for Machine Learning
Topic 2: Regression
- What is Regression
- Applications of Regression
- Least Square Error Minimization
- Data Pre-processing
- Bias vs Variance Trade-off
- Regression Methods with Regularization
- Logistic Regression
Topic 3: Classification
- What is Classification
- Applications of Classification
- Classification Algorithms
- Confusion Matrix
- Classification Performance Evaluation
Topic 4: Clustering
- What is Clustering
- Applications of Clustering
- Distance Measure
- Clustering Algorithms
- Clustering Performance Evaluation
- Anomaly Detection Problem
Topic 5: Principal Component Analysis
- Principal Component Analysis (PCA) and Dimension Reduction
- Applications of PCA
- PCA Workflow
Topic 6: Deep Learning
- What is Neural Network
- Activation Functions
- Loss Function Minimization
- Gradient Descent Algorithms and Learning Rate
- Deep Neural Network for Visual Recognition
- Improve Visual Recognition with Convolutional Neural Network
- The Future of AI
- AI Ethics
Assessment
- Written Exam
- Practical Exam
Course Info
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
Software Requirement
Please download and install the following software prior to the class
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
| Individual Sponsored Trainee | Employer Sponsored Trainee |
- Singapore Citizens or Singapore Permanent Residents of age 21 and above
- From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
- Trainee must pass all prescribed tests / assessments and attain 100% competency.
- We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.
- Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
- From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
- Trainee must pass all prescribed tests / assessments and attain 100% competency.
- We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.
| SkillsFuture Credit: |
- Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.
| PSEA: |
- To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
- Scroll down to "Keyword Tags" to verify for PSEA eligibility.
- If there is “PSEA” under keyword tags, the course is eligible for PSEA.
- And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
- Not all courses are eligible for PSEA funding.
| Absentee Payroll (AP) Funding: |
- $4.50 per hour, capped at $100,000 per enterprise per calendar year.
- AP funding will be computed based on the actual number of training hours attended by the trainee.
| SFEC: |
- If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SWDA would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
- Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
- SkillsFuture Enterprise Credit (SFEC) Microsite
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
- SWDA 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
- The candidate has the right to disagree with the assessment decision made by the assessor.
- When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
- If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
- If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
- If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
- 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.
- The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
- The candidate must lodge the appeal within 7 days, giving reasons for appeal
- The assessor can help the candidate with writing and lodging the appeal.
- he assessment manager will collect information from the candidate & assessor and give a final decision.
- A record of the appeal and any subsequent actions and findings will be made.
- An Assessment Appeal Panel will be formed to review and give a decision.
- The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
- The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
- Please click the link below to fill up the Candidates Appeal Form.
Job Roles
Job Roles
- Data Scientist
- Machine Learning Engineer
- Statistician
- R Developer
- Quantitative Researcher
- Bioinformatics Scientist
- Predictive Modeler
- Data Analyst (focusing on advanced analytics)
- Big Data Specialist (using R)
- Algorithm Developer
- AI Researcher (using R)
- Financial Quantitative Analyst
- Business Intelligence Specialist (with R expertise)
- Marketing Analytics Specialist (using R)
- Epidemiologist (utilizing machine learning).
Trainers
Trainers
Dr Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and more than a decade of academic and industry experience. He has taught at NTU, SUSS, Curtin University, and SP Jain School of Global Management, as well as serving as an IBM Data Science Instructor. His professional expertise spans machine learning, optimization, and quantitative methods, complemented by multiple IBM certifications in Python, R, TensorFlow, and advanced analytics. He is also the founder of DataFrens.sg, an open-source data science community that promotes applied machine learning in Singapore.
In his training, Dr Ang focuses on guiding learners through R-based machine learning workflows, including classification, clustering, and dimensionality reduction. He emphasizes the practical application of pattern recognition techniques to real-world datasets, ensuring learners gain both technical depth and applied problem-solving skills. By blending theory with hands-on coding exercises, Dr Ang equips participants with the knowledge to design and deploy machine learning models for research and business innovation.
Review
Customer Reviews (4)
- The instructor Mr Dwight was really helpful and engaging, thank you :) Review by Course Participant/Trainee
-
Perhaps having more examples and datasets to try out, going deeper into the mathematical and statistical concepts (Posted on 5/22/2023)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 - will recommend Review by Course Participant/Trainee
-
Trainer is very experienced and knowledgeable and generous in sharing his knowledge (Posted on 5/11/2023)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 - might recommend Review by Course Participant/Trainee
-
Learnt a lot but just too much info in 2 days. (Posted on 5/11/2023)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 - will recommend Review by Course Participant/Trainee
-
. (Posted on 2/2/2023)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
- Recommended Courses