Machine Learning and Generative AI: From Foundations to LLMs

As artificial intelligence continues to reshape industries, understanding both traditional machine learning and modern generative models has become an essential skill. This course demystifies the core concepts of AI, taking you from raw data to intelligent text generation without complex mathematical barriers. Through structured text-based explanations and practical Python code examples, you will transition from a curious beginner to a confident practitioner. You will read, analyze, and write foundational AI code, developing a clear mental model of how machines learn and generate content. What you'll learn: - Understand foundational AI terminology, including supervised learning, unsupervised learning, and neural network architectures. - Apply Python programming basics to clean, prepare, and structure data for machine learning tasks. - Build and evaluate classic machine learning models for classification and regression. - Explore the mechanics of Large Language Models (LLMs) and the fundamentals of prompt engineering. - Learn modern AI patterns like vector databases and retrieval-augmented generation (RAG) concepts. - Practice implementing clean, modern Python code with type hints to build robust AI workflows. The course starts with essential terminology and the basic intuition behind learning algorithms, then moves step-by-step through data preprocessing, neural networks, and finally, modern generative AI architectures. It is designed specifically for beginners with no prior experience in data science or machine learning. Start reading today to unlock the fundamentals of modern artificial intelligence.