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NLTK Complete Guide 📚

A comprehensive, modular tutorial series covering the Natural Language Toolkit (NLTK) library from fundamentals to advanced real-world applications.

Made with Claude Opus 4.5 Copilot 🤖

📋 Table of Contents

# Notebook Description
01 Introduction & Setup NLTK installation, downloading resources, basic usage
02 Text Processing Fundamentals Raw text handling, encoding, basic text operations
03 Tokenization Word/sentence tokenization, regex tokenizer, custom tokenizers
04 Stopwords & Text Cleaning Stopword removal, text cleaning pipelines
05 Stemming Porter, Lancaster, Snowball stemmers
06 Lemmatization WordNet lemmatizer, POS-aware lemmatization
07 POS Tagging Part-of-speech tagging, tagsets, custom taggers
08 Named Entity Recognition NER with NLTK, entity extraction, chunking
09 Chunking Chunk parsing, regex patterns, noun phrase extraction
10 N-Grams & Language Models Bigrams, trigrams, n-gram models, text generation
11 Frequency Distribution FreqDist, ConditionalFreqDist, text statistics
12 WordNet Synsets, semantic relations, word similarity
13 Sentiment Analysis VADER, SentiWordNet, sentiment scoring
14 Text Classification Naive Bayes, feature extraction, model evaluation
15 Corpus Management Built-in corpora, custom corpus creation, corpus readers
16 Advanced Topics CFG parsing, information extraction, optimization
17 Real-World Projects Summarization, keyword extraction, chatbot, Q&A

🚀 Getting Started

Prerequisites

  • Python 3.8+
  • Jupyter Notebook or JupyterLab

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/Python-NLTK.git
cd Python-NLTK

  2. Install dependencies:

    pip install -r requirements.txt

  3. Download NLTK data (run in Python):

    import nltk
nltk.download('all')  # Or download specific packages as needed

  4. Launch Jupyter:

    jupyter notebook

📦 Requirements

  • nltk - Natural Language Toolkit
  • matplotlib - Visualization
  • numpy - Numerical operations
  • jupyter - Notebook environment

📖 Learning Path

Beginner (Notebooks 01-05)

Start here if you're new to NLP. Learn text processing basics, tokenization, and text normalization techniques.

Intermediate (Notebooks 06-11)

Dive into linguistic analysis with POS tagging, NER, chunking, and statistical analysis of text.

Advanced (Notebooks 12-17)

Explore semantic analysis, machine learning classification, and build real-world NLP applications.

🎯 Features

  • ✅ Comprehensive Coverage - From basics to advanced topics
  • ✅ Hands-on Examples - Runnable code in every notebook
  • ✅ Practical Projects - Real-world applications included
  • ✅ Utility Classes - Reusable code components
  • ✅ Best Practices - Performance optimization tips

📝 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

Happy Learning! 🎉