AI Evolution: Knowledge Representation to Machine Learning

Artificial intelligence did not emerge overnight; it evolved through distinct historical waves defined by technological breakthroughs and conceptual shifts. Understanding how AI transitioned from symbolic logic to data-driven learning provides essential context for navigating today's tech landscape. This beginner-friendly course breaks down the core concepts behind artificial intelligence history, contrasting early knowledge representation with modern statistical techniques. You will start with fundamental terminology and basic definitions before examining why expert systems flourished and where they hit their limits. Through structured written modules, you will discover how modern algorithms process data to build practical intelligent systems. What you'll learn: Learn core AI terminology, foundational concepts, and historical developments; Compare Second Wave knowledge representation with Third Wave machine learning paradigms; Understand how rule-based expert systems store and utilize structured knowledge; Examine the mechanisms behind deep learning and neural network architectures; Explore contemporary advancements including generative AI and large language models; Apply conceptual frameworks to evaluate the strengths and limits of different AI approaches. The curriculum begins with clear explanations of basic terms before guiding you through historical developments and modern practical paradigms. Designed for complete beginners, no prior background in programming or advanced mathematics is required. Start reading today to build a clear, well-rounded understanding of artificial intelligence.