Foundations of Modern AI: Machine Learning, LLMs, and AI Agents

Artificial intelligence is transforming every industry, but understanding how these technologies actually work is the key to leveraging their true potential. This written course demystifies the core concepts of modern AI, taking you from a curious beginner to a confident conceptual thinker. By the end of this course, you will transition from simply using AI tools to understanding the underlying mechanics of machine learning, large language models (LLMs), and autonomous agent systems. You will learn how to: Understand the fundamental differences between supervised learning, unsupervised learning, and deep learning. Explore how Large Language Models (LLMs) process text, predict tokens, and generate human-like responses. Apply essential prompt engineering techniques to guide AI models toward accurate and context-aware outputs. Learn the basics of Retrieval-Augmented Generation (RAG) and how vector databases enhance AI memory. Discover the architecture of autonomous AI agents and how they plan, use tools, and execute complex workflows. The course begins with foundational terminology and early machine learning concepts before progressing systematically to generative AI architectures, modern LLM patterns, and the rise of autonomous agent frameworks. This course is designed entirely for beginners, requiring no prior programming experience or mathematical background. Start reading today to build a solid conceptual foundation in the technologies shaping our future.