Modern NLP: From Transformers to Retrieval-Augmented Generation (RAG)

Language is the ultimate interface, and modern neural networks have transformed how computers understand human text. If you want to build applications that can read, analyze, and generate text with human-like capability, mastering modern Natural Language Processing (NLP) is your essential first step. This text-based course guides you from the foundational rules of text processing to cutting-edge architectures. You will gain a deep conceptual understanding of how machines represent language, how attention mechanisms work, and how to implement retrieval-augmented systems to ground AI models in real-world facts. What you will learn: Understand foundational NLP concepts, text preprocessing, and tokenization techniques; Master the architecture of Transformers and the mechanics of self-attention; Build Retrieval-Augmented Generation (RAG) systems to connect language models with external knowledge; Apply modern text classification, sentiment analysis, and text generation techniques; Explore model interpretability to understand how neural networks make decisions; Write clean PyTorch code for NLP tasks using modern library conventions. You will start by exploring core terminology and classical text representation before moving on to deep learning models. Through clear written explanations and step-by-step code snippets, you will progress from basic sequence models to state-of-the-art transformer pipelines and RAG implementations. This course is designed for aspiring developers, data enthusiasts, and software engineers who want a clear, step-by-step introduction to modern NLP. A basic understanding of Python programming is recommended, but no prior experience with deep learning is required. Start reading today to unlock the potential of modern language models and build smarter text applications.