Foundational Machine Learning for Generative AI Systems

Generative AI is transforming technology, but building and working with these systems requires a solid grasp of the underlying machine learning architectures. This course provides a clear, text-based path to understanding the core mathematical and structural concepts behind modern artificial intelligence. You will transition from a curious learner to someone who understands how data flows through complex networks to generate text, images, and code. What you will learn: Understand the foundational principles of neural networks and backpropagation; Analyze how Convolutional Neural Networks (CNNs) process spatial data; Explore Recurrent Neural Networks (RNNs) and their role in sequential data; Master the Transformer architecture, self-attention mechanisms, and tokenization; Learn the basics of modern training pipelines, including fine-tuning and retrieval-augmented generation (RAG) concepts; Practice analyzing model architectures through detailed written walkthroughs and code-based structural examples. This course begins with essential terminology and the mathematical intuition of machine learning before guiding you through feedforward networks, specialized architectures, and the breakthrough transformer models that drive today's generative tools. It is designed specifically for beginners, software developers, and product managers who want to understand the mechanics of AI without needing a prior degree in data science. Start reading today to demystify the technology shaping our future.