Course Overview
The Machine Learning for Pattern Recognition & Anomaly Detection course is a professionally structured, applied machine learning training programme designed to equip learners with the capabilities required to identify patterns, detect irregularities, and interpret deviations in data-driven systems across enterprise and real-world contexts.
The course addresses both the foundational principles of pattern recognition and the practical application of machine learning techniques for anomaly detection, enabling organisations to move from reactive data monitoring to proactive insight discovery. Learners develop the ability to recognise normal behaviour, identify meaningful deviations, and support early-warning, risk mitigation, and decision-support systems.
The programme emphasises data understanding, feature engineering, supervised and unsupervised learning techniques, anomaly detection methodologies, model evaluation, and interpretability, ensuring that detection systems are accurate, robust, explainable, and operationally reliable.
Machine Learning for Pattern Recognition & Anomaly Detection is a focused, application-oriented ILT program designed to equip learners with the skills required to identify patterns, detect deviations, and uncover hidden structures in data using machine learning techniques.
Building on Python-based data handling and ML foundations, this course emphasizes supervised and unsupervised learning approaches, feature extraction, statistical reasoning, and model evaluation techniques commonly used in real-world anomaly detection scenarios such as fraud detection, quality monitoring, cybersecurity, and predictive maintenance.
The program balances conceptual understanding, algorithmic intuition, and hands-on implementation, enabling participants to confidently apply machine learning models to pattern recognition and anomaly detection problems.
Course Code: TGS-2026061576
Course Duration: 3 days
Course Fee: $1550.00
Learning Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of pattern recognition and anomaly detection
- Prepare and preprocess data for ML-based detection tasks
- Extract and engineer meaningful features from datasets
- Apply supervised and unsupervised learning algorithms
- Detect anomalies using statistical and ML-based methods
- Evaluate and interpret detection models using appropriate metrics
- Apply learned techniques to real-world use cases
Learning Outcomes
Participants will demonstrate the ability to:
- Analyze datasets to identify patterns and irregularities
- Implement classification and clustering algorithms in Python
- Build anomaly detection models using ML techniques
- Evaluate models using precision, recall, ROC, and error analysis
- Interpret model outputs and explain detection decisions
- Develop end-to-end ML workflows for anomaly detection problems
| TYPE | Singapore Citizens and
Singapore Permanent Residents |
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above | SME-sponsored
Local employees (i.e Singapore Citizens and Singapore Permanent Residents) |
| SkillsFuture
Funding (Baseline) |
SkillsFuture
Mid-career Enhanced Subsidy |
SkillsFuture
Enhanced Training Support For SMEs |
|
| Course Fee | $1550.00 | $1550.00 | $1550.00 |
| SkillsFuture Funding | $775.00 | $1085.00 | $1085.00 |
| Total Nett Fee | $775.00 | $465.00 | $465.00 |
| GST (9% of Course fee) | $139.50 | $139.50 | $139.50 |
| Total Fee Payable to | $914.50 | $604.50 | $604.50 |
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