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https://github.com/cyberfantics/naturallanguageprocessing

A comprehensive repository for the Natural Language Processing course, featuring lecture notes, slides, and practical implementations of key NLP concepts using Python and popular libraries.
https://github.com/cyberfantics/naturallanguageprocessing

chatbots hacktoberfest lemmatization nltk nltk-python spacy-nlp stemming tokenization transformer

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A comprehensive repository for the Natural Language Processing course, featuring lecture notes, slides, and practical implementations of key NLP concepts using Python and popular libraries.

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README 

        
# Natural Language Processing (NLP) Course

Welcome to the **Natural Language Processing (NLP)** repository! This repository contains course materials, including labs, lecture notes, and practice files, for the NLP course taught by **Mam Uzma**.



Google Drive for Slides: [Course Slides](https://drive.google.com/drive/folders/1jxcGj9lB-6ifeMAl_-p2KBQMH1dcy-G4?usp=sharing)



## 🗂 Repository Structure

The repository is organized into the following folders:



- **Labs**: Practical exercises and lab assignments for hands-on learning of NLP techniques and tools.

- **Lectures**: Lecture slides, notes, and explanations of the topics covered in the course.

- **Datasets**: Sample datasets used in labs and projects.

- **Projects**: End-of-course projects or advanced assignments integrating multiple NLP concepts.



## 🔧 Technologies and Tools Used

This course focuses on a wide range of NLP tools and libraries, including:



- **Python**: The primary language for all implementations.

- **NLTK (Natural Language Toolkit)**: For basic NLP operations like tokenization, stemming, and sentiment analysis.

- **SpaCy**: For efficient and advanced NLP pipelines.

- **TextBlob**: For simpler NLP tasks like part-of-speech tagging and text classification.

- **Pandas**: For handling and preprocessing datasets.

- **Scikit-learn**: For implementing machine learning models.

- **TensorFlow/PyTorch**: For building deep learning models for NLP tasks.



## 📚 Topics Covered

The course includes a variety of foundational and advanced NLP topics:



- Text Preprocessing: Tokenization, stemming, lemmatization, and stopword removal.

- Language Models: N-grams, Markov chains, and modern transformer-based models (BERT, GPT).

- Sentiment Analysis: Analyzing emotions and opinions in text data.

- Named Entity Recognition (NER): Identifying entities like names, dates, and locations in text.

- Text Classification: Using machine learning and deep learning for categorizing text.

- Machine Translation: Techniques and tools for translating text between languages.

- Question Answering and Chatbots: Developing interactive NLP applications.

- Advanced Topics: Transfer learning, fine-tuning, and generative models.



## 🚀 How to Use

1. Clone the repository:

   ```bash

   git clone https://github.com/cyberfantics/NaturalLanguageProcessing.git

   ```



2. Navigate to the desired folder (e.g., `Labs` or `Lectures`).



3. Follow instructions in the lab or project files to explore the content and run the code.



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## 🤝 Contributions

Contributions are welcome! If you have suggestions, improvements, or bug fixes, feel free to:



1. **Fork the repository**.

2. **Make your changes**.

3. **Create a pull request** with a clear description of your updates.



Alternatively, you can open an **issue** in the repository to discuss potential enhancements.



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## 📧 Contact

For questions or collaboration opportunities, feel free to reach out:



- **Email**: [[email protected]](mailto:[email protected])

- **GitHub**: [CyberFantics](https://github.com/cyberfantics)



Happy coding and learning! 😊