This beginner-friendly path introduces the core data processing techniques used in NLP and large language model applications. You will learn how raw text is cleaned, structured, tokenized, transformed, and stored so it can be used effectively in search, retrieval, training, and knowledge management workflows. The path covers foundational preprocessing methods such as text cleaning, bag-of-words, TF-IDF, embeddings, and modern tokenization approaches including BPE, WordPiece, and SentencePiece. You will also explore how chunking supports efficient processing of long documents and how vector databases enable structured retrieval. As you progress, you will examine scalable preparation strategies for large datasets, including data collection, deduplication, filtering, augmentation, and efficient storage. The focus is on building reliable pipelines that improve data quality and support downstream LLM performance.
