JavaScript seems to be disabled in your browser.
You must have JavaScript enabled in your browser to utilize the functionality of this website.

Home
IBF - Machine Learning 101 for Financial Trading

IBF - Machine Learning 101 for Financial Trading

28 Review(s) | Add Your Review

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.

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

Brochure

Download WSQ - Machine Learning 101 for Financial Trading

IBF-STS Accreditation - Up to 70% Funding

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Singaporeans below 40 yrs old and PR	Singaporeans above 40 yrs old
$900	$81.00	$531.00	$351.00

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

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.

Course Code: TGS-2023018794

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 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

Final Assessment

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

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
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

Dr. Alvin Ang: 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: Teh Siew Yee is a seasoned data analytics and digital transformation leader with over two decades of experience spanning banking, technology, and manufacturing industries. He has held senior positions at organizations such as Standard Chartered, Hewlett-Packard, TikTok, and SIA Engineering, where he led initiatives in AI governance, financial analytics, and business intelligence. Holding a Master of IT in Business (Artificial Intelligence) from Singapore Management University, he brings strong expertise in applying AI and machine learning to real-world financial and operational use cases.

In “Machine Learning 101 for Financial Trading,” Siew Yee focuses on helping learners understand how data-driven models can be applied to market forecasting, portfolio optimization, and algorithmic trading. His sessions emphasize the fundamentals of supervised and unsupervised learning, time-series analysis, and risk modeling using Python. With his experience in AI strategy and data-driven decision-making, he equips participants with the skills to design, evaluate, and deploy practical trading models that bridge analytics with financial insight.

Tan Woei Ming: Tan Woei Ming is a data scientist and AI engineer with over 15 years of experience in deep learning, predictive modeling, and real-time data automation, primarily in the semiconductor and industrial analytics sectors. He holds a Master’s in Intelligent Systems from the National University of Singapore and a First-Class Honours degree in Electrical and Electronic Engineering from Nanyang Technological University. His professional work includes developing AI-driven predictive systems and deploying machine learning pipelines for high-stakes environments.

In “Machine Learning 101 for Financial Trading,” Woei Ming brings his technical depth to guide learners through the end-to-end process of building trading models—from data preprocessing to feature engineering and model validation. His sessions focus on applying ML algorithms such as regression, decision trees, and reinforcement learning to trading data. By combining his strong engineering background with practical coding instruction, he helps learners translate algorithmic concepts into deployable trading intelligence.

Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is a data engineer and cloud AI specialist with over 18 years of experience in data analytics, software engineering, and intelligent systems design. A certified trainer and experienced practitioner, he has led teams in developing scalable data pipelines, implementing trading analytics systems, and integrating AI solutions into enterprise environments. His expertise bridges cloud architecture, algorithmic modeling, and data visualization for decision support.

In “Machine Learning 101 for Financial Trading,” Dwight helps participants understand how to operationalize machine learning in financial trading environments. His training covers topics such as data ingestion, model deployment, and the use of cloud-based ML tools for backtesting and prediction. By combining hands-on technical practice with real-world trading scenarios, he enables learners to build automated, efficient, and data-driven systems that enhance trading performance and insight.

Dr. Alfred Ang: 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.