How AI Works: Foundations of Machine Learning and Large Language Models

Artificial intelligence is transforming our world, yet its inner workings often seem like a black box. This course demystifies the technology behind the headlines, explaining how algorithms learn, adapt, and generate human-like responses. By reading this guide, you will transition from a curious observer to someone who understands the mathematical and logical foundations of AI. You will grasp how neural networks process data, how modern large language models are trained, and how systems make predictions, all without needing a background in advanced mathematics. What you'll learn: * Understand the core terminology and history of artificial intelligence and machine learning. * Explore how neural networks function and mimic human brain structures to process information. * Analyze the training process of large language models and how they predict the next word. * Discover the basics of prompt engineering and retrieval-augmented generation patterns. * Evaluate the ethical considerations, bias, and limitations of modern AI systems. * Learn how vector databases and embeddings help machines understand semantic meaning. The course begins with foundational concepts and key terminology before moving into the mechanics of neural networks, machine learning algorithms, and modern generative AI models. You will reinforce your learning through written conceptual exercises designed to solidify your understanding of how these systems operate. This course is designed for absolute beginners, professionals wanting to understand the tech behind AI, and curious minds with no prior programming or advanced math experience. Start reading today to confidently discuss, analyze, and navigate the rapidly evolving world of artificial intelligence.