NLP_

🧠 Natural Language Processing Concepts – Notebook Implementations

This repository is a curated collection of Jupyter notebooks demonstrating foundational and intermediate Natural Language Processing (NLP) concepts using real-world libraries such as SpaCy, NLTK, Scikit-learn, Gensim, and FastText.

The notebooks are structured to support learning, experimentation, and practical application of key NLP building blocks including tokenization, POS tagging, NER, embeddings, and text classification.

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📌 Contents

Notebook	Description
tokenizer.ipynb	Introduction to tokenization using SpaCy
stop_words.ipynb	Removing stop words from text
Bag_Of_Words.ipynb	Building Bag-of-Words representations
bag_of_n_grams.ipynb	N-gram generation and vectorization
tf-IDF.ipynb	Computing TF-IDF vectors using Scikit-learn
stemming&lemmatization.ipynb	Stemming and Lemmatization comparison (NLTK vs SpaCy)
POS_tagging.ipynb	Part-of-Speech tagging using SpaCy
NER.ipynb	Named Entity Recognition with SpaCy
spacy.ipynb	Overview of core SpaCy functionalities
spacy_lang_processing_pipeline.ipynb	Constructing custom NLP pipelines in SpaCy
fasttext_classification.ipynb	Text classification using FastText word embeddings
gensim_word_vectors_text_classification.ipynb	Word2Vec embedding-based classification using Gensim
news_clasification_spacy_word_vectors.ipynb	News text classification using SpaCy word vectors

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🛠️ Technologies

• Python 3.8+
• Jupyter Notebook
• SpaCy
• NLTK
• Scikit-learn
• Gensim
• FastText (via Gensim or Facebook)

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🧪 Getting Started

1. Clone the repository

git clone https://github.com/anjaliy11/NLP_.git
cd NLP_

2. Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate

3. Install dependencies
pip install -r requirements.txt

4. Run Jupyter
jupyter notebook
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## 📦 Dependencies
spacy
nltk
gensim
scikit-learn
matplotlib
jupyter
Then run:
python -m spacy download en_core_web_sm

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 ## 🎯 Use Cases
💡 Education and training on NLP fundamentals

🔍 Rapid prototyping of NLP preprocessing pipelines

📊 Experimentation with vectorization and classification techniques

🤖 Baseline development for text classification tasks