Programme Parameters
Advanced Postgrad
5 Months (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Natural Language Processing (NLP) Engineer Certification Program is a structured path to the NLP engineer role. You build from text preprocessing and embeddings through classic NLP tasks to modern transformers and large language models, then the engineering to ship them: fine-tuning, retrieval-augmented systems, and deployment. It culminates in a capstone NLP project. You finish credentialed and able to work as an NLP engineer, having built language systems end to end. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This certification program builds full natural-language-processing engineer competency — from text fundamentals and transformers to building and deploying real NLP and LLM systems.
📋 Course Objectives
1. Master text preprocessing and embeddings.
2. Build classification, NER and sequence models.
3. Apply transformers and large language models.
4. Build and deploy fine-tuned and RAG systems.
5. Deliver an NLP capstone project.
👥 Who Should Enroll?
• Aspiring NLP engineers
• Developers moving into language AI
• ML practitioners specialising in NLP
• Students targeting NLP careers
🚀 Key Learning Outcomes
• Full NLP-engineer competency.
• A deployed language-AI project.
• A credential for NLP-engineering roles.
• 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
Foundations and Preprocessing
• Tokenisation, normalisation and the languages where whitespace rules fail
• TF-IDF and n-gram baselines that remain competitive on small data
• Corpus construction, annotation guidelines and inter-annotator agreement
From Vectors to Transformers
• Word embeddings, contextual embeddings and what each fails to capture
• BERT-family encoders for classification, tagging and extraction
• Fine-tuning practice: learning rates, epochs and catastrophic forgetting
The Core Engineering Problems
• Named entity recognition, classification and span extraction
• Question answering and summarisation, extractive against abstractive
• Evaluation: F1, ROUGE, BLEU and why each correlates poorly with quality
Generative Components in a Pipeline
• Retrieval-augmented generation and when a smaller fine-tuned model is better
• Structured output, function calling and schema validation
• Cost, latency and the case for not using an LLM at all
Deployment and Portfolio
• Serving, batching and monitoring an NLP service in production
• Bias, toxicity and privacy in text data, including PII handling
• A defensible end-to-end project with evaluation evidence
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | NLTK |
| Covered Tool / Platform | spaCy |
| Covered Tool / Platform | Hugging Face Transformers |
| Covered Tool / Platform | Gensim |
| Covered Tool / Platform | BERT |
| Covered Tool / Platform | GPT |
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