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Master the foundations of Natural Language Processing, from text preprocessing and tokenization to word embeddings that capture semantic meaning. Learn to build text classification systems and understand the transformer architecture that powers modern language models like BERT and GPT.
Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. This field has undergone a dramatic transformation with the advent of deep learning, particularly the Transformer architecture introduced in 2017.
Traditional NLP relied heavily on hand-crafted features and rule-based systems. Modern NLP leverages neural networks that learn representations directly from raw text, capturing nuances of meaning, context, and linguistic structure that were previously impossible to encode manually.
The key insight driving modern NLP is that words and sentences can be represented as dense vectors (embeddings) in a continuous space where semantic relationships are preserved. Words with similar meanings cluster together, and analogies can be computed through vector arithmetic.
This chapter covers:
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Tokenization, vocabulary building, and TF-IDF — converting raw text into numerical representations for models.
Dense vector representations that capture semantic meaning — Word2Vec, GloVe, and contextual embeddings from transformers.
Classification and generation
Bag of words to fine-tuned transformers — sentiment analysis, spam detection, and multi-label categorization.
BERT, GPT, and T5 — pre-trained architectures that dominate modern NLP through self-supervised learning.
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