AI Fundamentals for Professionals

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Course Description

Artificial Intelligence (AI) is rapidly transforming how organizations operate, compete, and create value. From recommendation engines and virtual assistants to generative AI tools such as ChatGPT, AI technologies are becoming an integral part of modern business. As AI adoption accelerates across industries, professionals need a solid understanding of the technologies that power these innovations.

This self-paced online course provides a practical, non-technical introduction to the core technologies that form today’s AI landscape. Participants will explore the evolution of AI, understand the relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, and learn how these technologies are applied in real-world business settings. The course also examines the capabilities, limitations, opportunities, and risks associated with modern AI systems.

Designed specifically for non-technical audience such as managers, business professionals, and MBA students, this course focuses on building AI literacy rather than technical programming skills. By the end of the course, learners will possess the knowledge needed to engage confidently in AI-related discussions, evaluate AI opportunities, and make informed decisions about AI adoption within their organizations.

Course Objectives

Upon successful completion of this course, participants will be able to:

  1. Explain the relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.
  2. Describe the major applications and historical milestones of Artificial Intelligence.
  3. Differentiate between Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
  4. Explain how Machine Learning systems learn from data and improve over time.
  5. Identify the major types of Machine Learning and their business applications.
  6. Describe the Machine Learning development process from problem definition to deployment.
  7. Explain the role of Deep Learning and artificial neural networks in modern AI systems.
  8. Recognize key Deep Learning models and their practical applications.
  9. Evaluate the strengths, weaknesses, opportunities, and threats associated with Deep Learning.
  10. Explain the technological foundations of Generative AI.
  11. Identify major Generative AI tools, vendors, and application categories.
  12. Assess the capabilities and limitations of Generative AI in business contexts.
Course Overview

The course is organized into six modules and fifteen lessons. It begins by introducing the relationship between AI, Machine Learning, Deep Learning, and Generative AI. Learners then explore the evolution of AI, key applications, and future possibilities before diving deeper into Machine Learning and Deep Learning concepts. The course concludes with an examination of Generative AI, including its underlying technology, tools, capabilities, and limitations. Throughout the course, emphasis is placed on practical business relevance and managerial understanding rather than technical implementation.

Opening

This introductory module establishes the foundation for the entire course. Learners will explore the relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, understanding how these technologies build upon one another. The module provides a conceptual framework that will help learners place subsequent topics in the appropriate context.

Lessons

AI vs ML vs DL vs Gen AI

Artificial Intelligence

Artificial Intelligence has evolved from a research concept into a transformative business technology. In this module, learners will explore key AI applications, trace important milestones in AI history, and understand the distinctions between ANI, AGI, and ASI. The module provides both historical perspective and future outlook, helping learners appreciate where AI has come from and where it may be heading.

Lessons

What is AI? Key Applications of AI Brief History of AI ANI vs AGI vs ASI

Machine Learning

Machine Learning is the engine behind many modern AI applications. This module explains how machines learn from data, examines the major types of Machine Learning, and introduces the typical Machine Learning development process. Learners will gain an appreciation for how organizations use data-driven models to generate predictions, insights, and business value.

Lessons

Introduction to Machine Learning Types of Machine Learning Machine Learning Process

Deep Learning

Deep Learning has enabled many of the breakthroughs that characterize today’s AI revolution. This module introduces artificial neural networks and explores how Deep Learning differs from traditional Machine Learning approaches. Learners will also examine major Deep Learning models and evaluate the technology through a SWOT framework to understand its strengths, limitations, opportunities, and risks.

Lessons

What is Deep Learning? Key Deep Learning Models SWOT Analysis of Deep Learning

Gen AI

Generative AI has brought AI into mainstream business conversations by enabling machines to create text, images, audio, video, and software code. This module examines the technology behind Generative AI, surveys the rapidly evolving ecosystem of tools and vendors, and discusses both the capabilities and limitations of these systems. Learners will develop a realistic understanding of how Generative AI can be leveraged effectively and responsibly.

Lessons

Technology Behind Gen AI Gen AI Tools Landscape Capabilities and Limitations of Gen AI

Closing

Lessons

Summary

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