NLP Academy - Ultimate Tutorial


Project Snapshots

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NLP Academy Home
Text Preprocessing
Tokenization
Part of Speech Tagging
Named Entity Recognition
Sentiment Analysis
Text Summarization
Topic Analysis
Question Answering
Text Generation
Machine Translation
Zero-shot Classification
Vector Embeddings & Semantic Search

Project Technical Information

Project Name:
NLP Academy - Ultimate Tutorial
Project Type:
NLP Educational Platform Interactive Tutorials Comprehensive NLP Learning
Tech Stack:
Python Flask HTML/CSS/JS NLTK spaCy Transformers scikit-learn Matplotlib Seaborn Plotly Vedar Hugging Face Docker
NLP Topics Covered:
Text Preprocessing & Tokenization POS Tagging & Named Entity Recognition Sentiment Analysis & Text Classification Text Summarization & Generation Question Answering & Machine Translation Vector Embeddings & Semantic Search
Repository:
Repo: NLP_Academy

Project Summary

A comprehensive educational platform that provides tutorials and learning experiences for Natural Language Processing (NLP). The platform covers essential NLP topics from basic text preprocessing to advanced techniques like vector embeddings and semantic search, with practical implementations using modern NLP libraries.

Skills Demonstrated

Text Preprocessing Tokenization Part-of-Speech Tagging Named Entity Recognition Sentiment Analysis Text Summarization Question Answering Text Generation Machine Translation Zero-shot Classification Vector Embeddings Semantic Search

Tools Used

Python Flask NLTK spaCy Transformers scikit-learn Matplotlib Seaborn Plotly Vedar HTML/CSS/JS Hugging Face Docker

Solution

The NLP Academy provides a structured learning path through comprehensive modules covering essential NLP concepts. Students can explore topics from basic text preprocessing and tokenization to advanced techniques like transformer models, semantic search, and zero-shot classification, with practical implementations and real-world applications.

Approach

  1. Design: Create responsive web interface with navigation between tutorial sections.
  2. Content: Develop comprehensive tutorials for core NLP topics.
  3. Structure: Organize content from basic preprocessing to advanced techniques.
  4. Implementation: Build individual pages for each NLP concept with explanations.
  5. Examples: Include practical code examples and demonstrations for each topic.
  6. Navigation: Create seamless user flow between different tutorial modules.
  7. Deploy: Host the tutorial website for educational access.

Project Link(s)

Repository: NLP_Academy

Live Demo

Completed: 2025

Designed and Developed by Aradhya Pavan H S