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IBF - Machine Learning 101 for Financial Trading

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.

TSC Title: Data Analytics and Computational Modelling-3
TSC Code: FSE-DAT-3019-1.1

This funded course is temporarily unavailable for registration.

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

LO1: develop and analyse machine learning algorithms of financial data to derive patterns and trends
LO2: perform coding and testing of machine learning algorithms

Download Course Brochure

Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.
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.

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/home/for-individuals/resource-tools/myskills-portfolio 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/home/for-individuals/ibf-certification/why-be-ibf-certified.

IBF - Machine Learning 101 for Financial Trading

Course Code: TGS-2023018794

Registration temporarily unavailable This funded course is temporarily unavailable for registration.

33 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.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI applications courses.

Course FeeBefore Funding

$900.00 (GST-exclusive)

$981.00 (GST-inclusive)

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

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.

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

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

Written Exam
Practical Exam

Course Info

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

Software:

You will need the following for this course:

Python 3 – free
Visual Studio Code – free
Google Colab – free, runs in the browser with a Google account

Hardware: Windows PC or Mac laptop

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. Alfred Ang combines formal training in algorithmic trading with hands-on machine learning engineering. He completed an Executive Programme in Algorithmic Trading from QuantInsti and holds Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), along with AWS Certified Solutions Architect and AI Practitioner. He is also an IBM certified instructor for the AI Practitioners course. As founder of Tertiary Courses and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, he designs and delivers Skills Framework-aligned WSQ and IBF programmes and works as a full-stack AI developer and Agentic AI solution architect. His academic grounding is quantitative: a B.Sc (Hons) in Physics from NUS, where he topped his cohort, an MSc in Electrical and Electronic Engineering from NTU and a PhD in Electrical Engineering from NUS, followed by more than 20 peer-reviewed papers and more than 20 US patents as co-inventor. On GitHub, n8n-financeadvisor is a stock analysis dashboard with TradingView and TwelveData charts and live price and volume data, paired with an n8n-powered Telegram AI agent that gives Buy, Sell or Hold recommendations with its reasoning, and n8n-investmentadvisor adds an enquiry and FAQ chatbot workflow for an investment advisory site. His data-mining-ml-labs repository provides interactive Python and Streamlit labs that introduce data mining and machine learning step by step.

In IBF - Machine Learning 101 for Financial Trading, Dr. Ang introduces traders and finance professionals to machine learning in a way that stays close to market data and trading decisions. The first topic sets out the machine learning methodology: how it differs from deep learning, supervised versus unsupervised learning, choosing a target and features, training and prediction, and the metrics used to judge a model. The second topic works through supervised models, from linear and logistic regression and Naive Bayes to decision trees, random forests, XGBoost and neural networks, applying each to tasks such as predicting price direction or classifying market conditions. The third topic covers unsupervised learning with k-means and hierarchical clustering and principal component analysis, which help group securities, detect market regimes and reduce noisy indicators to their most informative components. His algorithmic trading training shapes the emphasis on what makes financial data difficult, including non-stationarity, look-ahead bias, overfitting to historical periods and the gap between a good backtest and live performance. He is clear that machine learning supports judgement and risk management rather than guaranteeing returns. Sessions are hands-on in Python, with learners building, testing and comparing models on market datasets and discussing why each model performs as it does. As an ACTA-certified trainer and DACE-certified curriculum developer, he paces the course for finance professionals who are new to coding, so that they leave able to evaluate machine learning claims critically as well as build simple models of their own. To explore his finance and machine learning projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Tan Woei Ming is a data scientist and AI engineer with over 15 years of experience in artificial intelligence, machine learning, deep learning and predictive modelling, primarily in the semiconductor and industrial analytics sectors. His professional work includes developing AI-driven predictive systems and deploying machine learning pipelines for high-stakes environments, together with real-time data automation. 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, and is known for translating complex AI technologies into practical business applications.

In IBF - Machine Learning 101 for Financial Trading, Woei Ming brings this technical depth to the end-to-end process of building trading models. Learners begin with machine learning methodology, including supervised versus unsupervised learning, targets and features, model training and evaluation metrics. They then apply supervised models such as linear and logistic regression, Naive Bayes, decision trees, random forest, XGBoost and neural networks, before exploring unsupervised techniques including K-means and hierarchical clustering and principal component analysis. By combining his engineering background with practical coding instruction, he helps participants move from data preparation to validated models they can evaluate critically on real market data.

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in machine learning, data analytics and AI applications. He has extensive hands-on experience developing predictive models and deep learning solutions using R, Python, TensorFlow and Keras, and his professional background includes finance, healthcare and customer analytics. Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and the risks of emerging technologies. He holds a Masters in Education from NTU and has eight years of teaching experience in international schools in Singapore and overseas.

In IBF Machine Learning 101 for Financial Trading, Dwight draws directly on his predictive modelling and finance analytics experience. He takes learners through the machine learning methodology, from targets and features to model training, prediction and evaluation metrics, before working through supervised models such as linear and logistic regression, Naive Bayes, decision trees, random forests, XGBoost and neural networks, followed by unsupervised techniques. His focus on the limitations as well as the strengths of models helps traders and finance professionals judge when a signal is trustworthy. Learners leave able to apply machine learning to market data and assess trading models with a critical, evidence-based eye.

Dr. Alvin Ang is a data science and AI expert specialising in optimisation, predictive modelling and financial analytics. He holds a PhD in Operations Research from Nanyang Technological University and has taught extensively at institutions such as SUSS, Curtin University and SP Jain School of Global Management, covering 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. He holds multiple IBM and Kaggle certifications in Python, R, data science and deep learning, combining technical mastery with applied research experience.

In IBF - Machine Learning 101 for Financial Trading, Dr. Ang takes a highly practical approach, guiding learners through building, testing and evaluating machine learning models for trading applications. Participants work through the machine learning methodology, including the differences between machine learning and deep learning, supervised and unsupervised learning, targets and features, model training and prediction, and the metrics used to evaluate models. They then apply supervised learning models such as linear and logistic regression to market data. By focusing on real-world case studies, financial datasets and algorithmic trading workflows, he equips traders and finance professionals to apply machine learning effectively in the financial services industry.

Teh Siew Yee is a data analytics and digital transformation leader with more than two decades of experience across the banking, technology and manufacturing industries. Siew Yee has held senior positions at Standard Chartered, Hewlett-Packard, TikTok and SIA Engineering, leading initiatives in AI governance, financial analytics and business intelligence. With a Master of IT in Business (Artificial Intelligence) from Singapore Management University, Siew Yee brings strong expertise in applying AI and machine learning to real-world financial and operational use cases, and is an ACLP-certified trainer known for turning complex concepts into clear, practical strategies.

In Machine Learning 101 for Financial Trading, Siew Yee connects that banking and financial analytics experience to the core machine learning toolkit that traders and finance professionals need. Learners begin with the methodology behind machine learning, including supervised versus unsupervised learning, targets and features, model training and evaluation metrics. They then work through supervised models such as linear and logistic regression, Naive Bayes, decision trees, random forests, XGBoost and neural networks, before applying unsupervised techniques such as k-means and hierarchical clustering and principal component analysis to market data. Siew Yee's sessions emphasise Python-based practice and sound model evaluation, so participants can judge when a model genuinely supports a trading decision.

Review

Customer Reviews (33)

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)