← Back to the catalogueNatural Language Processing
Intermediate to Advanced
Level
Classroom . Online . Hybrid
Mode
Course overview
A natural language processing course from text cleaning to transformer fine-tuning. Classical text processing and vectorisation are covered first so learners understand what transformer models replaced, then BERT-family models are fine-tuned for classification, named entity recognition, summarisation and question answering.
Build exercises include a resume classifier, a sentiment dashboard and a document question-answering service.
Who it is for
- Data scientists and ML engineers
- Developers building search or chat features
- Analysts working with survey and review text
- Postgraduate students
Prerequisites
Python and machine learning fundamentals.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
NLP Service of the Learner’s Choice
Programme sheet
The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.
Learning outcomes
01
Clean, tokenise and normalise text at scale.
02
Train classifiers for sentiment and topic tasks.
03
Fine-tune transformer models with Hugging Face.
04
Evaluate NLP systems with task-appropriate metrics.
05
Vectorise text with TF-IDF and word embeddings.
06
Extract entities and relationships from documents.
07
Build summarisation and question-answering pipelines.
Module structure
8 modulesStop words . Lemmatisation . Tokenisation . Stemming . Regular expressions
Tools — NLTK, spaCy
Bag of words . N-grams . GloVe . TF-IDF . Word2Vec . FastText
Module 03
Text Classification
Feature pipelines . Linear models . Metrics . Naive Bayes . Error analysis
Lab
Resume Classifier . Class Imbalance
Module 04
Sequence Labelling
Part-of-speech tagging . Rule-based patterns . Named entity recognition . Dependency parsing
Lab
Invoice Entity Extraction
Module 05
Transformers for NLP
Attention recap . Tokenizers . Inference cost . BERT and variants . Fine-tuning
Tools — Hugging Face Transformers
Module 06
Generation Tasks
Summarisation . Question answering . ROUGE and BLEU . Translation . Retrieval-augmented answering
Lab
Document Question Answering . Natural Language Processing (NLP)
Module 07
Conversational Systems
Intent and entity design . Dialogue state . Retrieval chatbots . LLM chatbots . Fallback handling
Serving models . Batch vs real time . Monitoring . Multilingual considerations
Assessment & certification
Module assignments and labs
25%
Final project and review
40%
Learners who complete all modules, submit the final project and clear the review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio, and interview preparation is included in the closing sessions.
Career outcomes . Roles this programme prepares for
NLP EngineerAI EngineerData ScientistChatbot DeveloperSearch Relevance Engineer
