Course Details
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 (Q&A)
- Written Assessment (PP)
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
Minimum Software/Hardware Requirement
Software:
You can download and install the following software:
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
Individual Sponsored Trainee | Employer Sponsored Trainee |
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SkillsFuture Credit:
PSEA:
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Absentee Payroll (AP) Funding:
SFEC:
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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.
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
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.
Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore.
His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.
Customer Reviews (4)
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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
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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
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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
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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