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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

Review

Customer Reviews (28)

Recommended 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
Overall i have better understanding of Machine Learning application for trading using python (Posted on 11/15/2025)
Recommended 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 11/15/2025)
Recommended 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
pace and teaching was easy to understand. trainer was patient (Posted on 11/15/2025)
Alvin is an excellent trainer! 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
Alvin is an excellent trainer! (Posted on 3/23/2025)
Excellent 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 is an excellent trainer with a lot of knowledge and experiences! He is very passionate about teaching and will share all that he knows to you. Machine Learning 101 for financial trading. (Posted on 3/21/2025)

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