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IBF Machine Learning 101 for Financial Trading

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.

Learning Outcomes

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

  • develop and analyse machine learning algorithms of financial data to derive patterns and trends
  • perform coding and testing of machine learning algorithms
Download Course Brochure

Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.
  • 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.

IBF - Machine Learning 101 for Financial Trading

Course Code: TGS-2023018794
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • IBF
  • SFEC
  • Absentee Payroll
  • MCES

28 Review(s) | Add Your Review

What's This Course About

Unlock the potential of machine learning in the financial trading sector with our IBF-STS Machine Learning 101 for Financial Trading course. Designed for both traders and finance professionals, this course demystifies the complex world of machine learning, teaching you how to analyze market data, develop predictive algorithms, and create automated trading strategies. You'll gain the knowledge and tools to identify profitable trading opportunities, manage risk, and maximize returns, making you an invaluable asset in today's fast-paced trading environment.

Master the application of machine learning algorithms for effective trading decisions. This course dives deep into key machine learning techniques such as regression analysis, clustering, and classification, giving you the skill set to construct, backtest, and deploy trading models. With hands-on exercises and real-world trading data, you'll be able to apply your newly-acquired skills instantly, boosting both your portfolio's performance and your market reputation.

Brochure

Download WSQ - Machine Learning 101 for Financial Trading

About IBF Certification

This course address the following Technical Skills and Competences (TSCs) and proficiency level:  

Data Analytics and Computational Modelling Level 3 TSC under Financial Services Skills Framework

Participants are encouraged to access the IBF MySKills Portfolio https://www.ibf.org.sg/programmes/Pages/MySkills-Portfolio.aspx to track their training progress and skills acquisition against the Skills Framework for Financial Services. You can apply for IBF Certification after fulfilling the required number of Technical Skills and Technical Competencies (TSCs) for the selected job role. 

Find out more about IBF certification and the application process at on https://www.ibf.org.sg/certification/Pages/Why-be-Certified.aspx.

WSQ Funding

Full Fee $900.00 Before GST
GST $81.00 9% of fee
Baseline Nett $531.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $351.00 SG age 40+ · 70% funded
IBF-STS Accreditation - Up to 70% Funding
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

Course FeeBefore Funding

$900.00 (GST-exclusive)
$981.00 (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 Methodology

Introduction to Machine Learning

Machine Learning vs Deep Learning

Supervised vs Unsupervised Learning

Machine Learning Implementation Steps

Target and Features

Model Training and Prediction

Metrics to Evaluate Machine Learning Models

Topic 2 Supervised Learning Models and Applications

The Linear Regression Model

Logistics Regression Model

Naïve Bayes Model

Decision Tree Model

Random Forest Model

XGBoost Model

Neural Network Model

Topic 3 Unsupervised Learning Models and Applications

K-Means Clustering Model

Hierarchical Clustering Model

Principal Component Analysis

Assessment

  • Written Exam
  • Practical Exam

Course Info

Promotion Code

Promo or discount cannot be applied to IBF-STS courses

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Basic Python Programming Knowledge
  • 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.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Softtware: Windows / Mac

Hardware: Laptop

Self-Sponsored Individuals

  • Up to 70% subsidy is available for Singapore Citizens and Permanent Residents of Singapore, physically based in Singapore. GST funding support will no longer be applicable for all courses.

Company-Sponsored Individuals

  • Up to 70% subsidy is available for Singapore Citizens and Permanent Residents of Singapore, physically based in Singapore. Please note:
  • The company must be a Financial Institution regulated by MAS or a FinTech firm certified by Singapore FinTech Association (SFA)
  • To register, please email your company name and your name to reachus@knowledgehut.com.sg.
  • For more information on the IBF subsidies and eligibility, please visit:  https://www.ibf.org.sg/programmes/Pages/IBF-STS.aspx

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’

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

Job Roles

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Research Scientist
  • Business Intelligence Specialist
  • Data Engineer
  • Software Developer (interested in ML)
  • Statistician
  • Predictive Modeler
  • AI Solutions Architect
  • Quantitative Researcher
  • Data Visualization Specialist
  • Analytics Consultant
  • Product Manager (focused on AI/ML products)
  • Innovation Specialist

Trainers

Trainers

Dr. Alvin Ang is a data science and AI expert with a PhD in Operations Research from Nanyang Technological University, specializing in optimization, predictive modeling, and financial analytics. He has taught extensively at institutions such as SUSS, Curtin University, and SP Jain School of Global Management, covering subjects including machine learning, R, Python for finance, and quantitative methods. Professionally, he has served as a data science trainer with IBM and Tertiary Infotech, delivering courses in AI, big data, and applied machine learning.
With multiple IBM and Kaggle certifications in Python, R, data science, and deep learning, Dr. Ang combines technical mastery with applied research experience. His training approach is highly practical, guiding learners through the process of building, testing, and evaluating machine learning models for trading applications. By focusing on real-world case studies, financial datasets, and algorithmic trading workflows, he equips participants with the skills to apply machine learning techniques effectively in the financial services industry.

Teh Siew Yee is an experienced adult educator and corporate trainer specializing in business communication, workplace effectiveness, and professional development. With years of experience across both corporate and training environments, she has helped learners enhance their skills in problem-solving, collaboration, and interpersonal communication. Her ability to translate complex concepts into clear, practical strategies ensures that participants can immediately apply their learning in the workplace. As an ACLP-certified trainer, Siew Yee delivers WSQ courses with a strong focus on learner engagement and workplace application. She integrates case studies, role-plays, and reflective exercises into her sessions, ensuring participants develop not only knowledge but also confidence in real-world contexts. By combining her corporate experience with adult education expertise, she empowers learners to improve workplace efficiency, strengthen teamwork, and achieve personal and organizational success.
Tan Woei Ming is an accomplished data scientist and AI engineer with over 15 years of experience in artificial intelligence, machine learning, and data-driven innovation. Holding a Master’s in Intelligent Systems from the National University of Singapore (NUS) and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led AI initiatives in predictive analytics, automation, and process optimization across the semiconductor and manufacturing industries. His expertise lies in translating complex AI technologies into practical business applications, enabling organizations to innovate through data insights and intelligent automation.
Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.

Dr. Alfred Ang is a specialist in artificial intelligence and data science with extensive experience applying machine learning models to real-world business and financial contexts. As Founder of Tertiary Courses Singapore, he has designed and delivered numerous WSQ and IBF-accredited programs in Python programming, data analytics, and AI adoption for financial services. His professional expertise covers areas such as predictive analytics, algorithmic modeling, and natural language processing, giving learners a strong foundation in understanding how AI transforms financial trading strategies.
With a PhD in Computer Science, Dr. Ang blends academic rigor with industry insights, helping participants bridge the gap between theory and application. He emphasizes practical, hands-on learning using financial datasets, enabling learners to build trading models that leverage supervised and unsupervised machine learning techniques. By combining deep technical knowledge with a learner-centric teaching style, Dr. Ang empowers financial professionals to adopt AI and machine learning for smarter, data-driven trading decisions.

Review

Customer Reviews (28)

Average Rating: 5.0/5 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
Interesting lesson as a python newbie! (Posted on 3/13/2026)
Average Rating: 3.7/5 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
N/A (Posted on 3/13/2026)
Average Rating: 5.0/5 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
Dr. Alvin Ang’s Machine Learning 101 for Financial Trading is a highly accessible and informative course for those interested in applying machine learning (ML) to finance. It covers foundational ML concepts like supervised and unsupervised learning, regression, and classification, all while linking them to practical trading scenarios. Dr. Ang does an excellent job of breaking down complex topics and contextualizing them in real-world trading applications, such as stock prediction, risk management, and algorithmic trading. The course is well-structured, starting with basics and progressing to more complex algorithms like decision trees and neural networks. The hands-on approach, using Python code and practical exercises, makes the content engaging and applicable. Dr. Ang’s clear teaching style and real-world examples make the learning experience enjoyable. While the course is beginner-friendly, a deeper exploration into advanced topics (e.g., deep learning for trading) could further enhance it for those with some prior knowledge. Additionally, more coverage on accessing financial data could help learners who are new to finance. Overall, it’s a great starting point for anyone looking to combine machine learning with financial trading, offering both theory and practical skills. (Posted on 3/13/2026)
Average Rating: 4.0/5 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
The course should state clearly the requirements for the course, otherwise course participants will have hard time digest it! (Posted on 3/13/2026)
Average Rating: 4.3/5 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
I am a frequent trader and being able to incoroporate what I leant in this machine learning will be invaluable for my journey. Dr Alvin is very knowledgeable and provides plenty of helpful and valuable resources which greatly improved my knowledge. Regarding training environment, I have taken the course virtually and the sound was quite intermittent sometimes, which result in not able to catch certain things clearly. Will be better if this is a classroom only option. (Posted on 3/13/2026)

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