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WSQ Pattern Recognition and Machine Learning with R

Course Information

  • Sessions 2 days
  • Duration 16 hrs
  • Level Beginner
  • Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Skills Framework

TSC Title
Pattern Recognition Systems
TSC Code
ICT-DIT-4026-1.1 TSC

Learning Outcomes

By end of the course, learners should be able to

  • LO1: Model pattern recognition problems suitable for machine learning.
  • LO2: Apply supervised regression techniques to predict pattern in the data.
  • LO3: Apply supervised classification techniques to classify pattern in the data.
  • LO4: Apply unsupervised clustering techniques to cluster patterns and detect anomaly in the data.
  • LO5: Apply principal component analysis as alternative method to detect pattern in the data.
  • LO6: Apply deep neural network and CNN models for visual recognition.
Download Course Brochure

Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.
  • OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - Pattern Recognition and Machine Learning with R

Course Code: TGS-2020504357
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

4 Review(s) | Add Your Review

What's This Course About

Dive into the intricate world of pattern recognition and machine learning with our WSQ-accredited course. This course is structured to take you from the essentials of R programming to the complexities of machine learning algorithms. You will learn to deploy pattern recognition techniques for data classification, delve into clustering methods, and explore predictive analytics. Real-world examples and projects are integrated into the curriculum for hands-on learning.

Whether you're a seasoned data scientist or a beginner eager to expand your skill set, this course is designed to equip you with the knowledge needed for the evolving field of data analytics. You'll leave with practical experience in applying machine learning algorithms using R, positioning you at the forefront of today’s data-driven landscape. Upgrade your analytics skills and stay ahead of the curve with our specialized training.

WSQ Funding

Full Fee $750.00 Before GST
GST $67.50 9% of fee
Baseline Nett $442.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $292.50 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used. Click here for SkillsFuture Credit submission

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year. Click here to submit UTAP

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding. Please inform us if you intend to use your PSEA funding.

To check for Post-Secondary Education Account (PSEA) eligibility for this course, Visit SkillsFuture (course code: TGS-2020504357)
  • Scroll down to “Keyword Tags” to verify for PSEA eligibility.
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form and email to us. 

Course FeeBefore Funding

$750.00 (GST-exclusive)
$817.50 (GST-inclusive)

Course Date

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

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

Final Assessment

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

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 SkillsFuture Singapore's (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 SkillsFuture Singapore's (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), SSG 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

  • 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.

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).

Review

Customer Reviews (4)

The instructor Mr Dwight was really helpful and engaging, thank you :) Review by Course Participant/Trainee
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
Perhaps having more examples and datasets to try out, going deeper into the mathematical and statistical concepts (Posted on 5/22/2023)
will recommend Review by Course Participant/Trainee
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
Trainer is very experienced and knowledgeable and generous in sharing his knowledge (Posted on 5/11/2023)
might recommend Review by Course Participant/Trainee
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
Learnt a lot but just too much info in 2 days. (Posted on 5/11/2023)
will recommend Review by Course Participant/Trainee
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
. (Posted on 2/2/2023)

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