WSQ – Building Advanced Machine Learning and AI Solutions with Pytorch

Topic 1: Overview of Deep Learning Application Domains

• AI and Deep Learning Landscape
• Deep Learning Application Domains
• AI Ethnics

Topic 2: Machine Learning for Regression and Classification

• General Concepts of Machine Learning and Neural Network (NN)
• Introduction to Pytorch Framework
• Build a Regression Model Using NN
• Build a Classification Model Using NN

Topic 3: Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• Build a Temporal Sequential Model Using RNN
• Build a Sentimental Analysis Model Using RNN

Topic 4: Convolutional Neural Network (CNN)

• Introduction to Convolutional Neural Network (CNN)
• Build a Image Classification Model Using CNN
• Techniques to Resolve Overfitting Issue

Topic 5: Application of Machine Learning to Signal Processing

• Digital Signal Processing
• Machine Learning Classification for Signal Processing
• Evaluation of Signal Processing Performance

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

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:

• PyTorch

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
• 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).

Dr Alvin Ang: Dr Alvin Ang is a ACTA certified trainer. Alvin Ang did his Ph.D., Masters and Bachelors from NTU, Singapore. Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background. He owns a startup externally

Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners.

Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis.

Terence Ee: Terence Ee is a ACTA certified trainr that has delivered IT training in Singapore and Myanmar. He has also facilitated faith formation courses for Christians in Singapore and Myanmar. As a trainer, his mission is to co-create insightful and actionable learning experiences with his learners.His current areas of focus include project management, information security management, quality management and office productivity applications.

Terence has more than 25 years of corporate IT experience. He has held senior management roles in the public and private sectors. He holds a Master of Science in Technology Management, a Bachelor of Science in Computer and Information Sciences, a Diploma in Family Education, and the Advanced Certificate in Training and Assessment (ACTA). Part of his spare time goes towards tutoring his children in their studies (while learning a thing or two along the way). He is also imparting to them the essential skills for thriving in a digital world.

Teh Siew Yee: Teh Siew Yee is a seasoned leader in data science and digital transformation, with over 20 years of experience driving organisational strategy, talent development, and the design of data ecosystems across Asia Pacific and global markets. He has successfully led cross-geographical teams and collaborated with industry leaders from the US, UK, China, India, Japan, South Korea, Australia, and beyond, focusing on leveraging data to achieve business objectives and optimize operations.

With expertise spanning predictive modeling, machine learning, deep learning, and IoT, he has hands-on experience in data architecture, engineering, and analytics. He has also developed comprehensive training programs, equipping all levels of an organisation— from C-suite to working-level employees— with the skills needed for digital transformation. His industry experience covers sectors such as tech, education, finance, aerospace, and eCommerce, making him a sought-after expert in data-driven business strategy.