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.

Full Time

This course is ideal for:

• IT professionals seeking a foundational understanding of AI
• Engineers, analysts, and technical teams supporting AI-driven projects
• Business and technology leaders exploring AI adoption
• Professionals preparing to transition into AI-related roles
• Students and fresh graduates interested in building AI literacy
• Anyone looking for a structured introduction to Predictive and Generative AI

3 Days

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