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WSQ - Building Your First Machine Learning Model with Python and Tensorflow

WSQ - Building Your First Machine Learning Model with Python and Tensorflow

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Embark on your journey into the realm of artificial intelligence with our WSQ-endorsed Basic Deep Learning with Tensorflow Keras course. This course provides an in-depth understanding of fundamental deep learning concepts, focusing on neural network architectures and how to implement them using Tensorflow Keras. With hands-on exercises and real-world examples, you'll learn the basics of designing, training, and tuning neural networks for various tasks, from natural language processing to image recognition.

By the end of this course, you’ll possess a foundational understanding of deep learning techniques. You'll be capable of using Tensorflow Keras to build and train basic neural network models. Whether you're looking to transition into the burgeoning field of AI, or you're a professional seeking to add deep learning to your skill set, this course is your stepping stone towards expertise in artificial intelligence.

Course Learning Outcomes

By the end of the course, learners will be able to:

LO1: Setup Tensorflow and Keras Deep Learning frameworks.
LO2: Understand and code Neural Network models for Regression.
LO3: Understand and code Neural Network models for Classification.
LO4: Understand and code Convolutional Neural Network models for Image Classification.
LO5: Understand and use pre-trained models for transfer learning.

Course Brochure

Download WSQ – Building Your First Machine Learning Model with Python and Tensorflow Brochure

Skills Framework

This course follows the guideline of Analytics and Computational Modelling ICT-DIT-3001-1.1 TSC under ICT Skills Framework

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 Funding

WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Baseline	MCES / SME
$750.00	$67.50	$442.50	$292.50

Baseline: Singaporean/PR age 21 and above
MCES(Mid-Career Enhanced Subsidy): S'porean age 40 & above

Upon registration, we will advise further on how to tap on the WSQ Training Subsidy.

You can pay the nett fee (after the WSQ training subsidy) by the following :

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-2019504744)

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 Code: TGS-2019504744

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.

Course Cancellation/Reschedule Policy

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
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

Topic 1: Introduction to Deep Learning

Machine Learning vs Deep Learning
Deep Learning Methodology
Overview of Tensorflow and Keras
Install and Run Keras

Topic 2: Introduction to Neural Network

What is Neural Network (NN)?
Loss Function and Optimizer
Build a Neural Network Model for Regression

Topic 3: Classification Model with Neural Network

One Hot Encoding and SoftMax
Cross Entropy Loss Function
Build a Neural Network Model for Classification

Topic 4: Convolutional Neural Network (CNN)

Introduction to Convolutional Neural Network?
ImageDataGenerator
Image Classification Model with CNN
Data Augmentation and Dropout

Topic 5: Transfer Learning

Introduction to Transfer Learning
Applications of Pre-Trained Models
Fine Tuning Pre-Trained Models

Final Assessment

Written Assessment - Short Answer Questions (WA-SAQ)
Practical Performance (PP)
Oral Questioning (OQ)

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

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

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

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

Machine Learning Engineer
Data Scientist
Deep Learning Researcher
AI Developer
Neural Network Designer
Computer Vision Engineer
NLP Engineer (branching into deep learning)
AI Product Manager (technical understanding)
Robotics Engineer (with AI components)
Bioinformatics Scientist (deep learning applications)
Medical Imaging Specialist (AI-focused)
Game Developer (AI-driven features)
Predictive Analytics Specialist
AI/ML Educator or Trainer
Autonomous Systems Developer.

Trainers

Quah Chee Yong - Quah Chee Yong is an ACLP-certified trainer with strong expertise in data science, machine learning, and natural language processing. He has served as Data Science Training Lead at MSITEK and previously as AI Solutions Lead at AiDeal Scan, where he developed predictive analytics, recommender systems, and NLP-driven applications using frameworks such as TensorFlow and Keras. His track record includes designing and delivering AI training programs for SAP, Temasek Polytechnic, and IMDA, equipping learners from both technical and non-technical backgrounds with essential machine learning skills.

With hands-on experience in building machine learning models, Quah has guided learners through data preparation, feature engineering, and model deployment using TensorFlow pipelines. He emphasizes practical, project-based learning where participants construct models from scratch, evaluate performance, and implement solutions in real-world contexts. His ability to bridge technical knowledge with accessible explanations ensures learners gain both confidence and competence in building their first machine learning models.

Dr Alvin Ang - Dr Alvin Ang holds a Ph.D. in Operations Research from Nanyang Technological University and brings extensive academic and industry expertise in machine learning, AI, and optimization.He has taught at NTU, SUSS, Curtin University, and SP Jain School of Global Management, as well as in professional training roles with IBM and Tertiary Infotech. An ACLP-certified trainer, Dr Ang has earned multiple IBM certifications in machine learning, deep learning, and TensorFlow, which he integrates into his course delivery.

With over a decade of teaching experience, Dr Ang emphasizes hands-on application of Python and TensorFlow for machine learning model development. Learners benefit from his structured approach, starting with supervised learning concepts before progressing to building and training neural networks. His teaching style blends theory with practical coding exercises, ensuring participants gain the skills needed to design, evaluate, and deploy their own machine learning models.

Solomon Soh - Solomon Soh is an experienced data scientist and AI trainer who has applied machine learning and deep learning in areas such as natural language processing, computer vision, and optimization. At IBM Singapore, he supervised more than 20 machine learning and deep learning projects, coaching teams on model design, data preprocessing, and deployment. His career includes data science roles at Workforce Optimizer and Certis Cisco, where he implemented forecasting, reinforcement learning, and predictive analytics solutions using TensorFlow and scikit-learn.

Certified in AI engineering and machine learning, Solomon has also served as lead instructor for data science bootcamps and corporate training programs. He specializes in guiding learners through their first steps in TensorFlow, helping them build, train, and evaluate neural network models. His teaching emphasizes hands-on coding, best practices in model development, and real-world case applications, equipping participants with the confidence to apply machine learning effectively.

Terence Ee - Terence Ee is a seasoned IT leader and independent trainer with more than 25 years of experience in technology management, systems integration, and digital transformation.He has served in senior leadership roles including Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he oversaw enterprise-level IT strategy and system development. Holding academic qualifications in computer science and technology management, Terence combines executive-level insight with technical expertise in software development and emerging technologies.

Since 2017, Terence has focused on training and consulting, helping professionals upskill in Python, data analytics, and machine learning. His training approach for TensorFlow emphasizes building a strong foundation in Python coding before guiding learners to develop, train, and deploy their first machine learning models. By combining his practical industry background with hands-on teaching, Terence ensures participants gain the applied knowledge to confidently implement machine learning in real-world organizational contexts.