Building the Foundations of Large Language Models (LLMs)

Large Language Models (LLMs) are transforming technology, but their internal workings often feel like a black box. This course starts at the very beginning, giving you a robust conceptual understanding of how these powerful models are constructed and trained. By the end of this course, you will have a deep understanding of the core Transformer architecture, tokenization, training phases, and modern deployment strategies necessary to analyze and work confidently with generative AI models. What you'll learn: * Understand the mathematical and conceptual foundations of the Attention mechanism and the core Transformer block. * Learn the complete LLM lifecycle, including pre-training, instruction fine-tuning, and alignment techniques. * Practice tokenization strategies and embedding generation methods central to efficient text processing. * Apply basic prompt engineering principles and understand the role of context windows during inference. * Configure efficient application patterns like Retrieval-Augmented Generation (RAG) for customized knowledge retrieval and factuality. This course begins with essential terminology and the history of sequence models before diving deep into the Transformer architecture and the training pipeline. We conclude by exploring modern deployment considerations and practical application patterns. This course is designed for absolute beginners in AI or machine learning who want a robust, conceptual foundation for LLMs. No advanced programming or deep learning experience is required. Start reading today and demystify the technology powering the AI revolution.