Chapter 10: Natural Language Processing Basics¶

Learn how to process and analyze text with AI—tokenization, TF-IDF, word embeddings, sentiment analysis, text classification, and named entity recognition.

Metadata¶

Field	Value
Track	Practitioner
Time	8–10 hours
Prerequisites	Chapters 1–9 (especially Chapter 9: Deep Learning Fundamentals)

Learning Objectives¶

Understand text representation: tokenization, vectorization, and word embeddings
Master classic NLP techniques: TF-IDF, word2vec, GloVe
Build text classification and sentiment analysis pipelines
Implement named entity recognition with spaCy
Use sequence models (RNNs, LSTMs) for NLP
Know when to use which technique and deploy simple NLP models

What's Included¶

Notebooks¶

Notebook	Description
`01_nlp_fundamentals.ipynb`	Tokenization, preprocessing, BoW/TF-IDF, word embeddings, first sentiment model
`02_nlp_classification.ipynb`	Deep learning for text, multi-class classification, NER, similarity and clustering
`03_nlp_advanced.ipynb`	Attention, seq2seq, transfer learning, production considerations, capstone

Scripts¶

text_preprocessing.py — Tokenization, stopwords, lemmatization, vocabulary, TextPreprocessor class
embedding_utils.py — Load embeddings, similarity, analogies, EmbeddingIndex
nlp_models.py — SentimentAnalyzer, TextClassifier, NERModel, TextSimilarity

Exercises¶

Problem Set 1 (notebook) — Tokenization, TF-IDF, word similarity, sentiment, vocabulary
Problem Set 2 (notebook) — LSTM classification, NER, clustering, multi-task, BERT preview
Solutions — In exercises/solutions/ (notebooks and solutions.py for CI)

Diagrams (Mermaid)¶

NLP pipeline, text representation methods, LSTM architecture

Read Online¶

10.1 Introduction — NLP fundamentals, preprocessing, TF-IDF, embeddings, sentiment intro
10.2 Intermediate — Deep learning for text, classification, NER, clustering
10.3 Advanced — Attention, transfer learning, production, capstone

Or try the code in the Playground.

How to Use This Chapter¶

Quick Start

Follow these steps to get coding in minutes.

1. Clone and install dependencies

git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters
pip install -r requirements.txt

2. Navigate to the chapter

cd chapters/chapter-10-natural-language-processing-basics
pip install -r requirements.txt
python -m spacy download en_core_web_sm

3. Download NLTK data (in Python or a notebook)

import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')

4. Launch Jupyter

jupyter notebook notebooks/01_nlp_fundamentals.ipynb

GitHub Folder

All chapter materials live in: chapters/chapter-10-natural-language-processing-basics/

Created by Luigi Pascal Rondanini | Generated by Berta AI