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Natural Language Processing: A Notebook-Based Introduction

Overview

mlciv/nlp-notebooks

Natural Language Processing: A Notebook-Based Introduction

mlciv/nlp-notebooks

Home
The Book
The Book
- Preface
- Roadmap
- License
Chapters
Chapters
- I — Language Model Foundations
  I — Language Model Foundations
- II — Machine Learning Foundation and NLP Tasks
  II — Machine Learning Foundation and NLP Tasks
  - Overview Overview
    Table of contents
    
    Chapters in this part
    
    What you'll be able to do
  - 2 · Text Classification
- III — Representations & Sequences
  III — Representations & Sequences
  - Overview
  - 3 · Embeddings & Sequence Models
- IV — Transformers & LLMs
  IV — Transformers & LLMs
- V — LLM Alignment and Agentic AI
  V — LLM Alignment and Agentic AI
Course
Course

II — Machine Learning Foundation and NLP Tasks¶

This part introduces the core machine-learning workflow for NLP — features, models, training, and evaluation — through a first end-to-end task.

Chapters in this part¶

Chapter 2: Text Classification — Sentence classification from the ground up, including logistic regression, multinomial logistic regression, and the core training/evaluation loop.

What you'll be able to do¶

Explore a dataset and choose appropriate evaluation metrics.
Build and evaluate binary and multiclass logistic-regression classifiers.
Reason about baselines, feature extraction, and the train/dev split.