Quick Hands-On NLTK tutorial for NLP in Python. NLTK is one of the most popular Python packages for Natural Language Processing (NLP). Easy to Start for Anyone.

This repository contains a collection of Machine Learning and NLP projects, including sentiment analysis with NLTK, text preprocessing, and deep learning models. It covers techniques like tokenization, stopword removal, lemmatization, rule-based analysis, and transformer models like BERT for practical NLP applications.

A collection of NLP projects exploring text analysis, sentiment detection, and language modeling using Python. Includes implementations with NLTK, spaCy, Scikit-learn, and Transformers. Focused on practical applications of machine learning and deep learning in natural language processing.

A complete guide to Data Science with practical projects in Machine Learning and NLP. Covers data analysis, model building, text processing, and real-world examples using Python, scikit-learn, pandas, NLTK, spaCy, and more. Ideal for beginners and intermediate learners.

This repository contains Python implementations of core NLP practicals, covering text preprocessing, cleaning, regex extraction, word frequency analysis, POS tagging, bigram language modeling, and text classification using Bag-of-Words, TF-IDF, and Word2Vec, with libraries like NLTK, spaCy and Scikit-learn.n

Repository of practical tutorials on Data Pipelines, APIs (FastAPI, Flask), NLP (NLTK), and AI with LangChain.

This repository contains all NLP lab assignments for the AIML course offered by SPPU, with practical implementations of core NLP concepts using Python, NLTK, spaCy, and other essential libraries.

This NLP guide covers essential topics, including tokenization, stopwords, stemming, lemmatization, POS tagging, chunking, NER, dependency parsing, and coreference resolution, with practical implementations using NLTK and SpaCy.

Intro_to_NLP_Python leverages WordNet and the Natural Language Toolkit (nltk) to provide an entry point into Natural Language Processing (NLP) with Python. Delve into fundamental concepts and practical applications, utilizing WordNet's lexical database and nltk's powerful tools for text processing and analysis.

This repository demonstrates practical NLP tasks using NLTK, including tokenization, stemming, lemmatization, POS tagging, named entity recognition, and parsing, with example scripts and datasets for learning and experimentation.

A practical NLP lab demonstrating text preprocessing with spaCy and NLTK, including tokenization, stemming, lemmatization, POS tagging, dependency parsing, stopword removal, and entity lookup via Google Knowledge Graph API.

Natural Language Processing Projects Practical NLP projects using real-world datasets and modern libraries. This repository includes: End-to-end NLP pipelines: data cleaning, tokenization, vectorization, modeling Preprocessing with NLTK, spaCy, and regular expressions Feature extraction using TF-IDF, Word2Vec, and BERT embeddings

Explore NLP techniques using NLTK and spaCy, with practical examples like text processing, word similarity analysis, and news categorization workflows. Includes ready-to-use notebooks, a preprocessed dataset, and setup instructions for seamless experimentation.

A comprehensive repository demonstrating core NLP concepts including tokenization, stemming, lemmatization, stopwords removal, POS tagging, Named Entity Recognition (NER), Bag of Words, N-grams, and TF-IDF with practical Python examples using NLTK and Gensim.

A collection of practical NLP tasks and experiments using Python and NLTK. This repository will be continuously updated with various text processing, analysis, and visualization projects as I explore different aspects of natural language processing.

End-to-end IR pipeline that crawls AI-related Wikipedia pages, preprocesses text (tokenization, stopwords, stemming, lemmatization), builds TF-IDF matrices, and ranks documents by cosine similarity to user queries. Built with Python, BeautifulSoup, NLTK, and Scikit-learn for practical NLP and IR experience.

A collection of essential Natural Language Processing (NLP) programs covering Regex, Text Preprocessing, POS Tagging, Named Entity Recognition (NER), Stemming, Lemmatization, Bag of Words, TF-IDF, Word Vectors (spaCy, Gensim), and more. Includes practical exercises and datasets for hands-on learning with Python libraries like spaCy, NLTK, Gensim