🧠 Natural Language Processing (NLP) Module

This repository contains work completed during my AI Engineer Bootcamp, focusing on essential concepts and practical implementations in Natural Language Processing (NLP).

📚 What I Learned

Throughout this module, I explored and implemented the following key topics:

• 🔤 Text Preprocessing
  Techniques such as tokenization, stopword removal, stemming, and lemmatization to prepare raw text for analysis.

• 🏷️ Part-of-Speech (POS) Tagging & Named Entity Recognition (NER)
  Identifying grammatical roles and extracting named entities from text using NLP libraries.

• 😊 Sentiment Analysis
  Classifying the emotional tone of text data using pre-built and custom models.

• 🧮 Text Vectorization
  Transforming text into numerical representations using methods like Bag-of-Words (BoW), TF-IDF, and word embeddings.

• 🧵 Topic Modeling
  Discovering abstract topics in large text corpora using models like Latent Dirichlet Allocation (LDA).

• 🛠️ Custom Text Classification
  Building and evaluating a custom text classifier using machine learning techniques.

🛠️ Tools & Libraries Used

• Python
• NLTK
• spaCy
• scikit-learn
• gensim
• pandas / NumPy
• Matplotlib / Seaborn

📌 Notes

This module showcases my understanding and hands-on skills in NLP, built through project-based learning in a real AI Engineer Bootcamp.