Natural Language Processing in Python: From Foundations to LLMs

Language is one of the most complex forms of data, but modern tools have made it more accessible than ever to analyze and generate. This course provides a structured path to mastering the techniques needed to turn unstructured text into structured knowledge. You will progress from basic text cleaning to implementing state-of-the-art transformer models, gaining the skills to solve real-world language tasks. By reading through conceptual explanations and practical code implementations, you will develop a professional intuition for how machines interpret human speech and writing. What you'll learn: - Understand fundamental NLP concepts and the historical shift toward deep learning - Clean and prepare text data using spaCy and pandas for various modeling tasks - Apply statistical methods and machine learning for sentiment analysis and classification - Explore the internal mechanics of transformers, including attention and embeddings - Master the basics of prompt engineering and retrieval-augmented generation (RAG) workflows - Use scikit-learn and Hugging Face to implement and evaluate language models The course starts with essential terminology and preprocessing workflows before transitioning into traditional machine learning and the modern era of generative AI. You will learn through clear written explanations and code examples that demonstrate how to apply these concepts immediately. This course is built for beginners and aspiring data enthusiasts who want a solid grounding in language technology without needing prior experience in AI. No specialized hardware is required to get started. Begin building your expertise in natural language processing today.