Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Natural Language Generation focuses on the half of NLP that produces language rather than consuming it. You trace the field from rule- and template-based systems through sequence-to-sequence neural models to today’s large language models, understanding what each can and cannot do. The course covers the practical levers of quality — decoding strategies, prompting, fine-tuning and controllability — and the hard problems of factuality, evaluation and safety. Working on real tasks such as summarisation and data-to-text, you leave able to build and evaluate a system that writes usefully and on brief. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers natural language generation — from templates and sequence-to-sequence models to controllable, prompt-driven generation with large language models.
📋 Course Objectives
1. Compare template, sequence-to-sequence and LLM-based generation.
2. Control output with decoding strategies and prompting.
3. Fine-tune models for summarisation and data-to-text.
4. Evaluate generated text for quality and factuality.
5. Address safety and controllability in generation.
👥 Who Should Enroll?
• Developers building generative-AI features
• Data scientists working on text generation
• Content and product teams using LLMs
• Students of language technology
🚀 Key Learning Outcomes
• The ability to build a text-generation system.
• A working NLG project such as a summariser.
• Sound judgement about generation quality and risk.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
NLP Foundations, Linguistics, and NLG Fundamentals
Analyze the fundamental concepts of linguistics and their application to natural language processing and generation • Develop a comprehensive understanding of the NLP pipeline, including text processing, tokenization, and feature extraction • Evaluate the strengths and limitations of rule-based and machine learning approaches to natural language generation
Text Preprocessing, Tokenization, and Feature Engineering
Implement text preprocessing techniques, including tokenization, stemming, and lemmatization, to prepare text data for NLP tasks • Design and develop feature extraction methods, such as bag-of-words and term frequency-inverse document frequency, to represent text data in a numerical format • Configure and optimize text preprocessing pipelines using popular NLP libraries and frameworks
Classical NLP Models and Statistical Methods
Develop and apply statistical models, such as n-gram and hidden Markov models, to natural language processing tasks • Analyze and evaluate the performance of classical NLP models, including their strengths and limitations • Implement and optimize statistical methods, such as maximum likelihood estimation and Bayesian inference, for NLP tasks
Deep Learning Architectures for NLG
Design and develop deep learning architectures, including recurrent neural networks and long short-term memory networks, for natural language generation tasks • Implement and optimize deep learning models using popular frameworks, such as TensorFlow and PyTorch • Evaluate the performance of deep learning models for NLG tasks, including their ability to generate coherent and contextually relevant text
Transformers, LLMs, and Attention Mechanisms
Implement and optimize transformer-based architectures, including BERT and RoBERTa, for natural language generation tasks • Develop and apply attention mechanisms, including self-attention and cross-attention, to improve the performance of NLG models • Analyze and evaluate the performance of large language models, including their ability to generate coherent and contextually relevant text
Model Evaluation, Fine-Tuning, and Optimization
Develop and apply evaluation metrics, including perplexity and BLEU score, to assess the performance of NLG models • Implement and optimize fine-tuning techniques, including transfer learning and domain adaptation, to improve the performance of pre-trained NLG models • Configure and optimize hyperparameters, including learning rate and batch size, to improve the performance of NLG models
Production NLP Systems, APIs, and Deployment
Design and develop production-ready NLP systems, including APIs and microservices, for natural language generation tasks • Implement and optimize deployment strategies, including containerization and cloud deployment, for NLP systems • Evaluate and ensure the scalability, reliability, and security of production NLP systems
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Transformers |
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