Programme Parameters
Advanced Postgrad
6 Weeks (60 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
Mastering Natural Language Processing is for those past the fundamentals who want depth and production capability. You go deep on transformer architectures and attention, advanced fine-tuning and adaptation, handling complex tasks like question answering, summarisation and information extraction, and the engineering to make NLP systems robust in production. The course emphasises the advanced techniques and judgement that distinguish an NLP specialist. You finish able to build sophisticated, production-grade NLP systems. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This advanced course deepens natural language processing — advanced transformer techniques, fine-tuning, and building sophisticated, production-grade NLP systems.
📋 Course Objectives
1. Master transformer architectures in depth.
2. Apply advanced fine-tuning and adaptation.
3. Handle QA, summarisation and extraction.
4. Engineer robust production NLP systems.
5. Evaluate and improve complex NLP tasks.
👥 Who Should Enroll?
• NLP practitioners seeking depth
• ML engineers specialising in language
• Researchers in NLP
• Students past introductory NLP
🚀 Key Learning Outcomes
• Advanced, production-level NLP capability.
• A sophisticated language-AI project.
• An NLP-specialist skill set.
• 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 Fundamentals
Analyze the fundamentals of linguistics and its application in Natural Language Processing (NLP) • Develop a comprehensive understanding of NLP concepts, including syntax, semantics, and pragmatics • Evaluate the role of linguistic theories in shaping NLP models and algorithms
Text Preprocessing, Tokenization, and Feature Engineering
Implement text preprocessing techniques, including tokenization, stemming, and lemmatization • Design and develop feature engineering pipelines for NLP tasks, including bag-of-words and term frequency-inverse document frequency (TF-IDF) • Configure and optimize text preprocessing workflows for improved model performance
Classical NLP Models and Statistical Methods
Develop and apply classical NLP models, including n-gram models and Hidden Markov Models (HMMs) • Analyze and evaluate the performance of statistical methods, including maximum likelihood estimation and Bayesian inference • Implement and optimize classical NLP algorithms, including Viterbi algorithm and forward-backward algorithm
Deep Learning Architectures for NLP
Design and develop deep learning architectures for NLP tasks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) • Implement and optimize deep learning models, including word embeddings and attention mechanisms • Evaluate the performance of deep learning architectures for NLP tasks, including language modeling and text classification
Transformers, LLMs, and Attention Mechanisms
Implement and optimize Transformer architectures, including BERT and RoBERTa • Develop and apply Large Language Models (LLMs) for NLP tasks, including language translation and text generation • Analyze and evaluate the role of attention mechanisms in improving model performance and interpretability
Model Evaluation, Fine-Tuning, and Optimization
Evaluate the performance of NLP models using metrics, including accuracy, precision, and recall • Fine-tune and optimize NLP models using techniques, including hyperparameter tuning and model pruning • Develop and apply model interpretability techniques, including feature importance and partial dependence plots
Production NLP Systems, APIs, and Deployment
Design and develop production-ready NLP systems, including data pipelines and model serving • Implement and deploy NLP APIs using frameworks, including Flask and Django • Configure and optimize NLP systems for scalability and reliability, including containerization and orchestration
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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