Question Generation using Natural Language processing

🎓 [Question Generation using Natural Language processing] 🚀

Headline: Auto generate assessments in edtech like MCQs, True/False, Fill-in-the-blanks etc using state-of-the-art NLP techniques!

Course Description: Are you ready to revolutionize the way educational content is assessed? 📚✨ This course by Ramsri Golla dives into the heart of Natural Language Processing (NLP) to teach you how to auto-generate quiz questions from any text content. With a focus on practical application, we'll traverse the spectrum of NLP tools—from foundational algorithms like word vectors to cutting-edge models like BERT and openAI GPT-2.

What You'll Learn:

• 🤖 Practical NLP Techniques: Transform text into a goldmine of quiz questions using libraries such as Spacy, NLTK, AllenNLP, and HuggingFace transformers.
• 🚀 Hands-On Learning with Google Colab: Gain access to free cloud computing and GPU training with ready-to-use Google Colab notebooks.
• 🧠 Real-World Problem Solving: Generate distractors for MCQ options, craft True/False questions, create Fill-in-the-blanks, and design Match the following questions using advanced NLP techniques.
• ✨ Model Deployment: Learn to deploy your trained models in a production environment, transforming them into ONNX format and creating lightweight docker containers for easy access.

Course Outline:

1. Generating Distractors for MCQs:

   • Utilize WordNet, ConceptNet, and Sense2vec to create effective distractors.

2. Creating True/False Questions:

   • Explore the use of sentence BERT, a constituency parser, and OpenAI GPT-2 to generate plausible true or false statements.

3. MCQ Generation with T5 Transformer:

   • Understand the workings of the T5 transformer model using HuggingFace's library and the SQUAD dataset.

4. Fill in the Blanks Questions:

   • Implement a Python Keyword Extraction Library, FlashText for keyword matching, and HTML ElementTree in Colab to generate engaging fill-in-the-blanks questions.

5. Match the Following Questions:

   • Learn to extract keywords, perform fast keyword matching with FlashText, and use BERT for word sense disambiguation (WSD).

6. Deploying Question Generation Models to Production:

   • Convert models to ONNX format, perform quantization, create docker containers using FastAPI, and deploy on Google Cloud Run.

Prerequisites: This course is designed for those with a solid grasp of deep learning concepts, proficiency in Python programming, and familiarity with NLP and PyTorch. A high-level understanding of the algorithms used will be provided, but the focus is on practical application rather than theoretical mathematics.

Join Ramsri Golla as he guides you through the intricacies of automating question generation in education using NLP—unlocking a new level of interactivity and personalization in learning experiences! 🌟

Enroll now to embark on this exciting journey at the forefront of edtech innovation with NLP! 🎓✨