(IBM) Machine Learning for Pattern Recognition & Anomaly Detection
About This Course
This course is designed for professionals who want to apply pattern recognition and machine learning techniques to business and operational challenges. Whether you're working in data analysis, technology, or operations, this course will equip you with the techniques across the machine learning pipeline, from data pre-processing to deep learning for vision recognition, along with an understanding of ethical considerations in AI systems.
What You'll Learn
LO1: Analyse pattern recognition and machine learning techniques to model applied problems
LO2: Analyse the types and steps involved in solving pattern recognition problems
LU2: Supervised Learning for Pattern Recognition
LO3: Apply supervised learning techniques to solve classification and prediction problems
LO4: Implement data pre-processing techniques for labelled data and assess their impact on pattern recognition methods
LU3: Unsupervised Learning and Anomaly Detection
LO5: Apply unsupervised learning techniques for clustering and anomaly detection
LO6: Implement advanced methods for pattern recognition using component analysis
LU4: Deep Learning for Pattern Recognition
LO7: Design and train deep neural networks for vision recognition problems
LO8: Analyse and improve deep learning models for pattern recognition tasks
LU5: Ethical Considerations in Pattern Recognition and AI
LO9: Evaluate the ethical implications of pattern recognition and AI systems
Entry Requirements
• Able to speak, listen, write, and read English with Numeracy skills at a minimum proficiency level 4 of the Employability Skills System (i.e., Workplace Literacy and Numeracy, WPLN).
• Possess Secondary School educational level qualification (O or N-Level).
• Learners should have basic programming knowledge, preferably in Python, and familiarity with fundamental statistical concepts. Prior experience working with data or datasets is recommended.