Foundations of LLMs and Deep Learning for Text Analysis

Understanding how computers process, analyze, and generate human language is one of the most valuable skills in technology today. This text-based course guides you from the absolute basics of text processing to the foundational concepts powering modern generative AI. By reading through our structured explanations and analyzing clear code examples, you will build a solid theoretical and practical foundation in deep learning for text, enabling you to understand how modern large language models function under the hood. What you'll learn: Understand core natural language processing concepts, tokenization, and word embeddings; Explore neural network architectures used for text analysis, including recurrent networks and transformers; Learn the foundational mechanics of large language models and attention mechanisms; Discover modern retrieval-augmented generation (RAG) patterns and vector database basics; Apply basic prompt engineering techniques to guide model outputs effectively; Practice analyzing text data using modern Python libraries and deep learning frameworks. The course begins with essential terminology and text-preprocessing fundamentals before moving step-by-step through deep learning architectures, transformer models, and practical generative AI applications. This course is designed for beginners, developers, and data enthusiasts who want to learn text analysis from scratch; no prior experience with deep learning is required. Start reading today to unlock the power of deep learning and large language models for text.