Machine Learning for Natural Language Processing

This repository provides hands-on implementations of essential Natural Language Processing (NLP) techniques using Machine Learning. It covers fundamental preprocessing steps, feature extraction methods, and advanced word embedding techniques to help build robust NLP models.

Table of Contents

1. Tokenization – Splitting text into meaningful units like words and sentences.

2. Text Preprocessing - Stemming, Lemmatization, and Stopwords – Cleaning text data by normalizing words and removing irrelevant tokens.

3. Parts of Speech Tagging - Identifying the grammatical category of words in a sentence.

4. Named Entity Recognition - NER – Extracting key entities like names, locations, and date/time from text.

5. Bow of Words – Representing text numerically in vector form using word frequencies.

6. Word2Vec Implementation – Learning word embeddings for capturing semantic relationships between words.

Libraries Required

Ensure you have the following Python libraries installed before running the notebooks:

pip install nltk pandas scikit-learn numpy gensim

Usage

Clone this repository and explore the notebooks to learn key NLP techniques through practical examples.