Question Generation using Natural Language processing

This course focuses on using state–of–the–art Natural Language processing techniques to solve the problem of question generation in edtech.

If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill–in–the–blanks, Match the following, etc.  In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques.

This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT–2, and T5 transformers to real–world use.

We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.

All the sections will be accompanied by easy to use Google Colab notebooks. You can run Google Colab notebooks for free on the cloud and also train models using free GPUs provided by Google.

Prerequisites:

This course will focus on the practical use cases of algorithms. A high–level introduction to the algorithms used will be introduced but the focus is not on the mathematics behind the algorithms.

A high–level understanding of deep learning concepts like forward pass, backpropagation, optimizers, loss functions is expected.

Strong Python programming skills with basic knowledge of Natural Language processing and Pytorch is assumed.