Welcome to the Natural Language Processing Lab Series!
This repository contains structured and hands-on labs designed to help you build a solid foundation in NLP - from basic preprocessing to advanced neural architectures.
Below is the list of labs covered in this course:
| Lab | Topic | Short Description |
|---|---|---|
| 01 | Web Scraping | Extract data from websites using requests and BeautifulSoup. |
| 02 | Exploring and Preprocessing Text Data | Clean and normalize text data for NLP tasks. |
| 03 | Language Models | Understand and implement probabilistic language models. |
| 04 | Vector Semantics and Embeddings | Represent words as vectors using co-occurrence-based methods. |
| 05 | Word Embeddings | Use dense word vectors like Word2Vec, GloVe, FastText. |
| 06 | Linear - Logistic Regression | Apply linear and logistic regression for NLP classification. |
| 08 | Neural Networks | Build feedforward neural networks for text tasks. |
| 09 | Recurrent Neural Networks (RNNs) | Model sequences and time-series using RNNs. |
| 10 | Sequence-to-Sequence (Seq2Seq) | Develop encoder-decoder architectures for NLP generation. |
| 11 | Transformer | Explore the Transformer architecture used in modern NLP models. |
Clone this repository:
git clone https://github.com/pdz1804/NLP_Lab.git
cd NLP_LabThis repository is for educational purposes only. Use freely and responsibly.