Natural Language Processing Applications
Why colleges run this
Text is the most abundant data in business; NLP skills let students build search, classification and sentiment tools that employers immediately value.
Course outcomes
CO1
Preprocess raw text through tokenisation and normalisation
K3CO2
Represent text numerically using TF-IDF and embeddings
K3CO3
Build a text classifier and evaluate its accuracy
K3CO4
Apply a pre-trained transformer model to a language task
K3CO5
Deploy a text-classification application on real data
K3Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Text foundations4 h
Theory
Tokenisation, normalisation, stemming and lemmatisation
Lab
Preprocess a raw text corpus
Output
Preprocessing notebook
02Representations4 h
Theory
Bag-of-words, TF-IDF, word embeddings
Lab
Vectorise text and inspect similarity
Output
Representation notebook
03Text classification4 h
Theory
Sentiment, topic labelling, evaluation
Lab
Build and evaluate a classifier
Output
Classifier notebook
04Sequence tasks4 h
Theory
Named-entity recognition, part-of-speech basics
Lab
Extract entities from documents
Output
Entity-extraction notebook
05Modern NLP4 h
Theory
Transformer models, using pre-trained models
Lab
Apply a pre-trained model to a task
Output
Pre-trained-model notebook
06Application capstone4 h
Theory
Packaging a text application on real data
Lab
Ship a text-classification application
Output
Deployed text application
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment