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 🤖
| # | 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 |
- Python 3.8+
- Jupyter Notebook or JupyterLab
-
Clone this repository:
git clone https://github.com/yourusername/Python-NLTK.git cd Python-NLTK -
Install dependencies:
pip install -r requirements.txt
-
Download NLTK data (run in Python):
import nltk nltk.download('all') # Or download specific packages as needed
-
Launch Jupyter:
jupyter notebook
nltk- Natural Language Toolkitmatplotlib- Visualizationnumpy- Numerical operationsjupyter- Notebook environment
Start here if you're new to NLP. Learn text processing basics, tokenization, and text normalization techniques.
Dive into linguistic analysis with POS tagging, NER, chunking, and statistical analysis of text.
Explore semantic analysis, machine learning classification, and build real-world NLP applications.
- ✅ 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
This project is open source and available under the MIT License.
- NLTK Project - Natural Language Toolkit
- NLTK Book - Official NLTK documentation
Happy Learning! 🎉