Generative AI and NLP: Building Foundational Language Models

Understanding how machines process, interpret, and generate human language is the cornerstone of modern artificial intelligence. This text-based course guides you through the core concepts of Natural Language Processing (NLP) and the mechanics of foundational generative AI models. You will transition from understanding basic text representation to implementing and evaluating language models using PyTorch. By studying structured explanations and clear code snippets, you will gain a practical grasp of how modern language models are built, trained, and adapted for real-world tasks. What you'll learn: - Understand key NLP terminology, text preprocessing, and foundational language concepts. - Convert text into numerical features using bag-of-words, one-hot encoding, and word embeddings. - Implement and train neural network architectures for language modeling and translation using PyTorch. - Explore the transition from classic recurrent models to modern transformer-based architectures. - Apply basic prompt engineering and retrieval-augmented generation (RAG) patterns to extend model capabilities. - Evaluate model performance using standard NLP metrics and validation techniques. The journey begins with essential terminology and mathematical representations of text before moving into hands-on PyTorch implementations of classification and translation models. You will then progress to modern generative paradigms, exploring how large-scale foundational models generate coherent text. This course is designed for aspiring AI developers, data enthusiasts, and programmers new to NLP. No prior background in deep learning is required, though basic familiarity with Python is helpful. Start reading today to build your foundation in generative AI and language modeling.