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 Processing takes you from raw text to working language models. You start with the essential groundwork — tokenisation, normalisation, and turning words into numbers with TF-IDF and word embeddings — then build models for the classic tasks: text classification, named-entity recognition and sentiment analysis. From there you move to the modern era, fine-tuning pretrained transformers such as BERT for state-of-the-art results with transfer learning. Every topic is grounded in real corpora, so you finish able to frame an NLP problem, choose an appropriate method, and ship a model that reads text usefully. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers natural language processing end to end — text preprocessing, embeddings, classification, sequence models and transformer methods — applied to real text.
📋 Course Objectives
1. Preprocess, tokenise and embed text for modelling.
2. Build text-classification, sentiment and named-entity models.
3. Apply sequence models to language tasks.
4. Fine-tune pretrained transformers such as BERT.
5. Evaluate NLP models with task-appropriate metrics.
👥 Who Should Enroll?
• Developers and data scientists moving into NLP
• Researchers working with text data
• Engineers building chatbots, search or text analytics
• Students specialising in language technology
🚀 Key Learning Outcomes
• The ability to build an NLP model for a real text problem.
• A working transformer-based NLP project.
• The judgement to pick the right method per task.
• 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 NLP Fundamentals
Analyze linguistic structures and their applications in natural language processing • Develop a comprehensive understanding of NLP fundamentals, including syntax, semantics, and pragmatics • Evaluate the role of linguistics in shaping NLP models and their performance
Text Preprocessing, Tokenization, and Feature Engineering
Configure text preprocessing pipelines to handle noise, normalization, and feature extraction • Implement tokenization techniques, including word-level, subword-level, and character-level tokenization • Design feature engineering strategies to enhance model performance and generalizability
Classical NLP Models and Statistical Methods
Implement Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) for sequence labeling tasks • Analyze the strengths and limitations of classical NLP models, including n-gram models and decision trees • Develop a deep understanding of statistical methods, including maximum likelihood estimation and Bayesian inference
Deep Learning Architectures for NLP
Design and implement Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequence modeling • Configure Convolutional Neural Networks (CNNs) and Transformers for text classification and language modeling tasks • Evaluate the performance of deep learning architectures on various NLP tasks and datasets
Transformers, LLMs, and Attention Mechanisms
Implement self-attention mechanisms and Transformer architectures for machine translation and text generation • Analyze the role of Large Language Models (LLMs) in NLP, including their applications and limitations • Develop a comprehensive understanding of attention mechanisms, including multi-head attention and hierarchical attention
Model Evaluation, Fine-Tuning, and Optimization
Evaluate NLP models using metrics such as accuracy, F1-score, and perplexity • Implement fine-tuning techniques, including transfer learning and domain adaptation • Optimize NLP models using hyperparameter tuning, regularization, and early stopping
Production NLP Systems, APIs, and Deployment
Design and deploy production-ready NLP systems using containerization and orchestration tools • Implement RESTful APIs for NLP models using frameworks such as Flask and Django • Configure and manage NLP pipelines using workflow management tools such as Apache Airflow
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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