AI Vibe Coding for Advanced Data Modeling
About This Course
AI Vibe Coding for Advanced Data Modeling equips participants with practical skills to build advanced predictive models using deep learning and AI-assisted coding techniques. Through natural-language instructions and AI coding assistants, learners will generate, explain, test, debug, and refine code for neural network models, making advanced data modelling more accessible and efficient.
Participants will begin with an overview of deep learning, exploring fundamental concepts such as tensors, neural network architectures, activation functions, loss functions, optimisers, gradient computation, and model training. They will then develop neural networks for regression to predict continuous outcomes and neural networks for classification to categorise data and support data-driven decision-making.
The course progresses to Convolutional Neural Networks (CNNs), where participants will learn to build models for image and pattern recognition tasks. Learners will also explore Transformers and self-attention, understanding how attention mechanisms enable models to identify relationships and contextual patterns within complex and sequential data.
Throughout the course, participants will use AI Vibe Coding to accelerate model development, troubleshoot errors, optimise model performance, and interpret results. By the end of the course, learners will be able to apply deep learning techniques to develop, evaluate, and refine advanced data models for prediction, classification, pattern recognition, and other real-world applications.
What You'll Learn
- apply machine learning principles to gain business insights.
- aggregate data to help test problem .
- apply predictive data modeling techniques to identify underlying trend and patterns in data using neural networks.
- develop prototype classification model using machine learning techniques to gain new insight from data.
- identify patterns using convolutional neural network model to derive insights and make decision.
- use data visualisation tool to create interactive visualizations of data.
Course Outline:
Topic 1 Overview of Deep Learning
Topic 2 Neural Network for Regression
Topic 3 Neural Network for Classification
Topic 4 Convolutional Neural Network
Topic 5 Transformer and Self-Attention
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• 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.
• Minimum 18 years old
Target age group: 21-65 years olf