The AI Fundamentals course provides a comprehensive and practical understanding of how Artificial Intelligence is applied in real-world organizational settings. Designed to bridge the gap between AI concepts and business implementation, the course explores key technologies such as machine learning, deep learning, natural language processing, generative AI, and multimodal AI and how they create value across industries.

A central focus of the program is its final module, which empowers participants to evaluate AI’s strategic, operational, and ethical impact within an enterprise. It introduces essential frameworks and tools to ensure AI initiatives are aligned with business goals and deployed responsibly.

Module 1: AI Concepts in Business and Industry

This applied module focuses on translating foundational AI concepts into real-world business and industry contexts. Participants will learn to assess different types of AI, evaluate machine learning strategies, and understand the practical impact of deep learning and AI implementation. The module also covers:

• Differences between weak, strong, and general AI in practical and theoretical scenarios
• Selecting appropriate machine learning techniques for domains like healthcare, finance, and retail
• Analysing deep learning, neural networks, and use cases like image recognition and NLP
• Assessing AI’s role in automation, augmentation, and decision support in organizations

Module 2: Core AI Technologies in Practice

This module focuses on applying key AI technologies like NLP, Computer Vision, and Deep Learning in real-world scenarios. It covers:

• Practical use of NLP for chatbots and sentiment analysis to improve communication
• Evaluation of Computer Vision applications such as facial recognition and object detection
• Application of deep learning models (CNNs, RNNs, GANs, Autoencoders) for tasks like image classification and media generation
• Use of NLP techniques like tokenization, POS tagging, and named entity recognition
• Real-world NLP applications in virtual assistants, search engines, and industries like healthcare and legal automation

Module 3: Generative AI and Large Language Models

This module explores the practical aspects of Generative AI and Large Language Models (LLMs), focusing on training, prompt engineering, and deployment. It covers:

• Stages of LLM training, including pre-training, fine-tuning, and Reinforcement Learning from Human Feedback (RLHF), with techniques like zero-shot and few-shot learning
• Principles of prompt engineering to enhance output quality, focusing on clarity and context
• Understanding transformer architectures, including self-attention and positional encoding
• Evaluation of compute and infrastructure trade-offs in LLM development and deployment
• Exploration of emerging trends in generative AI, including synthetic data and alignment challenges

Module 4: Multimodal AI Concepts

This module covers the integration of text, images, and audio in multimodal AI systems, focusing on practical applications and challenges. It includes:

• Basics of multimodal AI, objectives, and the distinction from single-modal approaches
• Applications like image captioning, VQA, and robotics, showcasing how modality fusion boosts performance
• Techniques such as early, late, and hybrid fusion, and challenges in data synchronization
• Benefits and limitations, including better contextual understanding and issues like noise and compute complexity
• Emerging trends in unified architectures and continual multimodal learning research

Module 5: Evaluating AI’s Impact in the Organisation

This module helps participants assess AI’s strategic, operational, and ethical impact in organizations. It covers frameworks for aligning AI with business goals, managing change, and ensuring responsible deployment. Topics include:

• Aligning AI strategy with business objectives and assessing readiness in infrastructure, leadership, and culture
• Evaluating solution design models (build vs. buy, hybrid) and the AI adoption lifecycle
• Managing organizational change, fostering collaboration, and supporting transformation through upskilling
• Measuring AI success with KPIs, financial and operational outcomes, and continuous improvement
• Ensuring ethical, secure AI deployment by addressing bias and ensuring regulatory compliance (e.g., GDPR)
• Implementing AI governance practices with ethics boards, audits, and risk management

• Business Leaders and Decision-Makers: Executives, managers, and entrepreneurs who want to understand how AI can drive innovation, optimize operations, and create competitive advantages in their organizations.
• Technology Professionals: Developers, engineers, data scientists, and IT specialists who aim to enhance their technical expertise by exploring AI methodologies, tools, and frameworks.
• Students and Academics: Learners from undergraduate and postgraduate programs in technology, business, and related fields, as well as educators seeking to deepen their knowledge of AI concepts and trends.
• Industry Specialists: Professionals in fields such as healthcare, finance, retail, and manufacturing who want to explore AI’s impact on their sectors and learn how to leverage AI technologies effectively.
• AI Enthusiasts and Curious Learners: Individuals with a passion for technology and innovation who want to explore the foundations of AI and its transformative potential across industries.
• Policy Makers and Regulators: Government officials, policy advisors, and legal professionals who need to understand AI’s implications for governance, compliance, and ethics.

This course is accessible to both technical and non-technical audiences, requiring no prior experience in AI or programming, and is ideal for anyone eager to understand the fundamentals of AI and its potential to shape the future.

The AI Fundamentals course concludes with an official APMG Examination, designed to validate the participant’s understanding of core AI concepts and principles. This structured assessment ensures that learners have grasped the foundational knowledge required to engage with AI confidently. Below are the key details about the examination:

• Material Allowed: This is a closed book exam. Study materials, including the course guide, are not permitted during the examination.
• Exam Duration: The exam is 60 minutes long. Candidates taking the exam in a language other than their native or working language receive an extra 25% of time, extending the duration to 75 minutes.
• Marks and Scoring: The exam consists of 40 multiple-choice questions, each worth 1 mark. There is no negative marking, and unanswered questions will receive no marks. To pass, participants must score 26 marks (65%) or more. An elevated pass mark of 30 marks (75%) is required for individuals aspiring to become trainers.
• Complexity: The exam includes questions at Bloom’s Levels 1, 2 and 3. Bloom’s Level 1 focuses on recalling facts, terms, and basic concepts, such as defining AI or identifying its key components. Bloom’s Level 2 requires grasping the meaning of concepts, such as explaining how machine learning differs from traditional programming or summarizing the role of neural networks in AI. Bloom’s Level 3 involves using acquired knowledge in practical contexts, such as solving a problem using AI algorithms or developing a basic machine learning model for a specific task.

This examination is designed to confirm that participants have achieved a solid foundation in Artificial Intelligence, enabling them to apply their knowledge effectively and confidently in real-world scenarios.