Introduction Artificial Intelligence (AI) is one of the most transformative technological fields of the 21st century. It encompasses the development of computer systems capable of performing tasks that normally require human intelligence such as reasoning, problem-solving, learning, perception, and decision-making. In recent decades, AI has evolved from a theoretical concept into a practical and integral part of modern society. It powers digital assistants, autonomous vehicles, medical diagnostics, and countless other applications that shape how individuals and organizations interact with technology. This module introduces students to the foundations of Artificial Intelligence, tracing its origins, examining its major types, and establishing the conceptual framework for understanding advanced AI systems and applications.

Introduction Machine Learning (ML) is a critical branch of Artificial Intelligence that enables computers to learn and make decisions without being explicitly programmed. It focuses on developing algorithms that can identify patterns, extract insights, and make predictions from data. In today’s digital era, Machine Learning powers numerous technologies from personalized recommendations on streaming platforms to fraud detection in financial systems and disease diagnosis in healthcare. This module introduces learners to the fundamental concepts, categories, workflow, and applications of Machine Learning, establishing a strong foundation for deeper exploration into data-driven technologies.

Introduction Machine Learning can be broadly divided into two major learning paradigms, Supervised Learning and Unsupervised Learning. Both methods enable computers to learn patterns from data, but they differ in the kind of data used, the objectives pursued, and the nature of the outputs produced. This module provides students with a comprehensive understanding of these two fundamental learning types, their working principles, key differences, and real-world applications. Through this, learners will gain the foundational knowledge required to select and apply appropriate algorithms in solving data-driven problems.

Introduction Deep Learning represents one of the most powerful and transformative areas within Artificial Intelligence and Machine Learning. It focuses on algorithms inspired by the structure and function of the human brain, known as Artificial Neural Networks (ANNs). Unlike traditional Machine Learning models that rely heavily on manual feature extraction, Deep Learning models automatically learn intricate patterns and representations from large amounts of data. This has led to breakthroughs in fields such as image recognition, speech processing, autonomous driving, and natural language understanding. This module introduces learners to the foundational principles of Deep Learning, the structure and components of Neural Networks, types of advanced Deep Learning models, and the tools and frameworks that power them.

Introduction Artificial Intelligence (AI) and Machine Learning (ML) have moved beyond research laboratories into everyday life, transforming industries, economies, and human experiences. From personalized recommendations on streaming platforms to predictive healthcare systems, these technologies are redefining how people live, work, and communicate. However, with great power comes great responsibility. As AI systems gain influence in critical decision-making hiring, lending, medical diagnosis, and security ethical questions about fairness, privacy, accountability, and transparency become increasingly important. This module explores real-world applications of AI and ML and the ethical considerations that accompany them. Learners will understand both the potential and the moral duties tied to the use of intelligent systems.