Overview
The AI Professional course provides a comprehensive introduction to the core concepts of modern Artificial Intelligence, covering both Predictive AI and Generative AI. This course helps learners build a strong grounding in how contemporary AI systems work, the business value they deliver, and the risks and governance considerations that come with adoption.
Participants will explore common AI learning approaches, model training processes, neural network fundamentals, and real-world applications across industries. The course also introduces key generative AI architectures such as Transformers, GANs, and VAEs, along with best practices for responsible AI implementation.
By the end of the course, learners will have the foundational knowledge needed to confidently engage with AI initiatives and pursue further AI specialist pathways.
Course Description & Learning Outcomes
1. Foundations of Predictive AI
Predictive AI business and technology drivers
Key benefits of Predictive AI in enterprise environments
Common risks and challenges of using Predictive AI
Business problem categories addressed by AI systems
Types of Predictive AI and where they are applied
Common learning approaches:
Supervised learning
Unsupervised learning
Reinforcement learning
Continuous and semi-supervised learning
Understanding predictive model training and the step-by-step training loop
Functional applications of Predictive AI, including:
Computer vision
Pattern recognition
Robotics
Natural language processing (NLP)
Speech recognition and understanding (NLU)
Introduction to AI models and neural networks
2. Foundations of Generative AI
Generative AI business and technology drivers
Benefits and opportunities enabled by Generative AI
Common risks and challenges of using Generative AI
Business problem categories addressed by Generative AI solutions
Understanding models, algorithms, and neural network fundamentals
Types of Generative AI systems
Training generative models and understanding the training loop
Key Generative AI architectures:
Generative Adversarial Networks (GANs)
Variational Encoders (VAEs)
Transformer models
Steps to building AI-driven systems
Generative AI best practices for real-world adoption
Recommended Prerequisites
There are no formal prerequisites for this course.
However, participants will benefit from having:
Basic familiarity with IT systems and digital technologies
A general interest in AI applications and emerging technology trends
No programming or mathematical background is required, as concepts are taught in an accessible, non-technical manner
Schedule
End Date: 05 Jun 2026, Friday
Location: 3 Kallang Jct, #04-02 Vanguard Campus, Singapore 339265, 339265
Pricing
Skills Covered
PROFICIENCY LEVEL GUIDE
Beginner: Introduce the subject matter without the need to have any prerequisites.
Proficient: Requires learners to have prior knowledge of the subject.
Expert: Involves advanced and more complex understanding of the subject.
- Information Systems (Proficiency level: Beginner)
