What this phase covers

Natural Language Processing (NLP) is about teaching a model to read, understand, and generate human language. Chapter 16 of Hands-On Machine Learning frames this as a journey: start with raw text, turn it into numbers a neural network can use, feed those numbers through a sequence model, and — eventually — replace the sequence model entirely with attention.

This phase follows that same arc:

  flowchart TD
  A["Raw text"] --> B["Text Preprocessing
tokenize, stem, lemmatize"]
  B --> C["Bag of Words & TF-IDF
sparse counts"]
  B --> D["Word Embeddings
dense vectors"]
  C --> E["Sentiment Analysis
classic + RNN models"]
  D --> E
  D --> F["Named Entity Recognition
sequence labeling"]
  E --> G["The Transformer Architecture
self-attention, multi-head attention"]
  F --> G
  G --> H["Sequence-to-Sequence Learning
encoder-decoder, machine translation"]

Phase 5 topics

1. Text Preprocessing (Tokenization, Stemming, Lemmatization)
2. Bag of Words (BoW) & TF-IDF
3. Word Embeddings (Word2Vec, GloVe)
4. Sentiment Analysis Tutorial
5. Named Entity Recognition (NER)
6. The Transformer Architecture
7. Sequence-to-Sequence Learning (Machine Translation)

Why this phase matters

Every large language model you use today — GPT, BERT, and their descendants — traces back to the ideas in this phase:

• text has to become numbers before a model can touch it (preprocessing, BoW/TF-IDF, embeddings)
• sequence models (RNNs) were the first serious attempt at “understanding” text in order
• attention let models look directly at any word instead of squeezing everything through a single hidden state — and that single idea unlocked the Transformer, BERT, and GPT

By the end of this phase you’ll understand not just how to call TfidfVectorizerTfidfVectorizer or a Hugging Face model, but why each step in the pipeline exists.