Foundations of Generative AI and Large-Scale Models

Generative AI is transforming industries, yet the underlying technology often feels like a black box. Understanding how these massive systems actually work is the first step to leveraging them effectively in your career. This course demystifies the mechanics of modern artificial intelligence, taking you from foundational AI concepts to the cutting edge of self-supervised learning. You will gain a clear, conceptual understanding of the models driving tools like ChatGPT and Copilot, without needing a background in complex programming or advanced mathematics. What you'll learn: 1. Understand the evolution of artificial intelligence and the shift from traditional machine learning to foundation models. 2. Explain how self-supervised learning allows models to learn from massive, unlabeled datasets. 3. Analyze the differences between supervised learning, reinforcement learning, and modern generative architectures. 4. Explore real-world applications of foundation models across business, creative industries, and scientific discovery. 5. Grasp the fundamentals of prompt engineering and modern retrieval-augmented generation patterns. 6. Evaluate the ethical implications, limitations, and future trajectory of generative AI systems. You will start by exploring key terminology and the historical context of AI development before diving into the mechanics of self-supervised training. From there, you will study how these models are applied practically to solve complex business problems and accelerate research. This course is designed for beginners, business professionals, and curious learners who want to understand the technology shaping our future. No prior programming or computer science experience is required. Start reading today to build a strong conceptual foundation in the world of generative AI.