Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Business Managers
• Project Managers
• Digital Transformation Leaders
• Data Analysts
• IT Professionals
• Innovation Managers

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand the fundamental concepts and terminology of Artificial Intelligence.
• Differentiate between AI, Machine Learning, Deep Learning, and Generative AI.
• Identify common AI applications across various industries.
• Explain how machine learning models are trained, evaluated, and improved.
• Assess ethical, legal, and governance considerations in AI adoption.
• Develop a roadmap for implementing AI initiatives within their organizations.

DAY 1

Foundations of Artificial Intelligence

• Introduction to AI and its evolution
• Key AI concepts and terminology
• Types of AI and intelligent systems
• AI technologies and enabling components
• Business applications and industry use cases
• AI trends and future developments

DAY 2

Machine Learning Fundamentals

• Introduction to machine learning
• Supervised, unsupervised, and reinforcement learning
• Data preparation and quality considerations
• Model training and validation concepts
• Performance measurement and evaluation
• Practical machine learning examples

DAY 3

Generative AI and Emerging Technologies
Fundamentals of Generative AI
Large Language Models (LLMs)
Prompt engineering principles
AI-powered content generation
Conversational AI and virtual assistants
Opportunities and limitations of Generative AI

DAY 4

AI Applications and Implementation

• Natural Language Processing (NLP)
• Computer Vision fundamentals
• Predictive analytics and intelligent automation
• AI in operations, finance, HR, and customer service
• AI project lifecycle and implementation planning
• AI adoption challenges and success factors
   

DAY 5

Responsible AI and Future Strategy

• AI ethics and responsible use
• Bias, fairness, and transparency
• AI governance and compliance considerations
• Risk management for AI initiatives
• Building an AI-ready organization
• Exercise and AI adoption roadmap