This repository contains a comprehensive collection of Natural Language Processing (NLP) concepts, text preprocessing techniques, feature extraction methods, and practical projects implemented using Python.
It provides a structured learning path from fundamental NLP concepts to real-world text processing and Natural Language Understanding using popular Python libraries.
- Introduction to NLP
- Text Preprocessing
- Tokenization
- Stop Words Removal
- Stemming
- Lemmatization
- Text Cleaning
- Regular Expressions (Regex)
- Bag of Words (BoW)
- TF-IDF
- Word2Vec
- Text Vectorization
- Feature Extraction
- Sentiment Analysis
- Text Classification
- NLP Projects
- Python
- NLTK
- spaCy
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Jupyter Notebook
Natural-Language-Processing/
│
├── 00_Datasets/
├── 01_Text_Preprocessing/
├── 02_Tokenization/
├── 03_Stop_Words/
├── 04_Stemming/
├── 05_Lemmatization/
├── 06_Regular_Expressions/
├── 07_Bag_of_Words/
├── 08_TF_IDF/
├── 09_Word2Vec/
├── 10_Text_Vectorization/
├── 11_Text_Classification/
├── 12_Sentiment_Analysis/
├── 13_Projects/
│
├── README.md
├── requirements.txt
├── LICENSE
└── .gitignore
By exploring this repository, you will learn:
- Clean and preprocess textual data
- Convert text into numerical representations
- Apply feature extraction techniques
- Understand word embeddings
- Build NLP pipelines
- Perform sentiment analysis
- Develop text classification models
- Work with real-world NLP datasets
Install the required dependencies:
pip install -r requirements.txtThis project is licensed under the MIT License.
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