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Indian Institute of Technology Guwahati

Post Graduate Certificate in Natural Language Processing

Indian Institute of Technology Guwahati via Coursera MasterTrack

Overview

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In this programme, you will first learn fundamental concepts and ideas in natural language processing (NLP), and build familiarity with the latest research. You will develop an in-depth understanding of both the algorithms available for processing linguistic information and the underlying computational properties of natural languages. The focus will be on deep learning approaches: implementing, training, debugging, and extending neural network models for a variety of language understanding tasks. You will progress from word-level and syntactic processing to question answering and machine translation. For the final project, you will apply a complex neural network model to a large-scale NLP problem.

As you advance through the programme, you’ll learn how to:
- Process raw text for NLP and formulate NLP problems mathematically.
- Analyse deep learning-based NLP models.
- Use machine learning and natural language processing to solve real-world problems across various industries.
- Use cross-lingual and multilingual models.

College graduates and working professionals wanting to specialise or enter the field of natural language processing will find this programme useful.

Syllabus

Course 1: Introduction to Natural Language Processing
- In this course, you will learn the fundamentals of natural language processing (NLP) and its linguistic aspects, core algorithms for solving basic tasks, and statistical and shallow machine learning models for several-NLP based tasks.

Course 2: Neural Networks for NLP
- In this course, you will learn about neural network models and apply the learnings to applications like hate speech detection, Q&A systems, chatbots and dialogue systems.

Course 3: Advanced Topics in NLP
- In this course you will learn about advanced and recently evolving topics in NLP such as efficient variants of transformers, multilingual and multimodal NLP, ethical issues in NLP, and domain specific applications.

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