Module 01: Foundations of NLP & Text Representation

Chapter Overview

How do machines learn to read? This chapter traces the evolution of text representation from counting words to understanding meaning. We start with the fundamental challenge of turning raw human language into numbers, work through classical techniques like Bag-of-Words and TF-IDF, then explore the revolution sparked by Word2Vec and dense word embeddings.

Along the way, you will build a complete text preprocessing pipeline, train word embeddings from scratch, explore the famous king/queen analogy, and see how contextual embeddings (ELMo) paved the road to the transformer models that power every modern LLM. Understanding this progression is essential: the entire history of NLP is a quest for better representations of meaning, and each technique you learn here is a building block for everything that follows.

Learning Objectives

Explain the evolution of NLP from rule-based systems to modern LLMs and why each transition happened
Build a complete text preprocessing pipeline using spaCy and NLTK
Implement and compare Bag-of-Words, TF-IDF, and one-hot encoding, and articulate their limitations
Explain how Word2Vec, GloVe, and FastText create dense word representations and why they work
Train a Word2Vec model from scratch and explore word analogies
Explain why static embeddings fail for polysemous words and how ELMo introduced contextual embeddings
Articulate the "big picture" of how text representation evolved toward transformers and LLMs

Sections

Prerequisites

Module 00: ML & PyTorch Foundations (especially sections on neural networks and gradient descent)
Python proficiency (functions, classes, list comprehensions)
Basic linear algebra: vectors, dot products, matrix multiplication
Familiarity with NumPy and basic scikit-learn usage