NLP Foundations: Machine Learning to Large Language Models

Natural Language Processing (NLP) is the driving force behind modern technology, enabling systems to read, analyze, and generate human language. Understanding these core concepts is essential for anyone looking to work with text data and modern artificial intelligence. This text-based course guides you from the absolute basics of linguistics and text preprocessing to machine learning models and modern generative AI patterns. By reading through structured explanations and code snippets, you will gain the confidence to analyze text and implement foundational NLP pipelines. What you'll learn: Understand core NLP concepts, including tokenization, stemming, and parts-of-speech tagging; Apply classic machine learning algorithms to text classification and sentiment analysis; Explore the architecture of modern Large Language Models (LLMs) and transformer networks; Implement basic prompt engineering and Retrieval-Augmented Generation (RAG) concepts; Practice working with vector databases to perform semantic search; Write clean Python code for text processing using modern standards and type hints. The course starts with foundational terminology, basic linguistic concepts, and text cleanup techniques. From there, you will progress to training machine learning models on text data, before exploring modern LLM architectures and vector-based search techniques. This course is designed for absolute beginners to NLP, requiring only a basic familiarity with Python programming. Start reading today to unlock the potential of natural language processing and modern language models.