Natural language processing & AI – Project based learning

GMUSD64

Credits: 2.00

Prerequisites : Basic understanding of Python programming and machine learning concepts

Course Duration : 30 Hours / 12 Weeks

Start Date : 2024-03-12

End Date : 2024-03-23

Seats Remaining : 60

Domain: Computer Science, Information Technology, and Linguistics students interested in NLP and AI

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Aim & Summary

The course covers fundamental NLP concepts such as text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and topic modeling. Participants will also explore AI algorithms such as machine learning classifiers, sequence-to-sequence models, and transformers for NLP tasks. Through project-based learning, participants will apply NLP and AI techniques to solve practical problems, such as text classification, language translation, chatbot development, and information extraction. By working on hands-on projects, participants will gain valuable experience in building and deploying NLP and AI systems.

Course Content
Week 1	Introduction to Natural Language Processing and AI
Week 2	Text Preprocessing Techniques: Cleaning, Tokenization, and Normalization
Week 3	Part-of-Speech Tagging and Named Entity Recognition
Week 4	Sentiment Analysis and Opinion Mining
QUIZ 1
Week 5	Topic Modeling: Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA)
Week 6	Introduction to Machine Learning for NLP: Supervised and Unsupervised Learning
Week 7	Text Classification with Naive Bayes, Support Vector Machines, and Neural Networks
Week 8	Sequence-to-Sequence Models for Language Translation
QUIZ 2
Week 9	Chatbot Development with Rule-Based Systems and Sequence Models
Week 10	Advanced Topics in NLP: Transformers and BERT
Week 11	Information Extraction and Named Entity Recognition
Week 12	Project Development: Building and Deploying NLP and AI Systems
PROJECT REPORT

Course Content

Week 1

Introduction to Natural Language Processing and AI

Week 2

Text Preprocessing Techniques: Cleaning, Tokenization, and Normalization

Week 3

Part-of-Speech Tagging and Named Entity Recognition

Week 4

Sentiment Analysis and Opinion Mining

QUIZ 1

Week 5

Topic Modeling: Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA)

Week 6

Introduction to Machine Learning for NLP: Supervised and Unsupervised Learning

Week 7

Text Classification with Naive Bayes, Support Vector Machines, and Neural Networks

Week 8

Sequence-to-Sequence Models for Language Translation

QUIZ 2

Week 9

Chatbot Development with Rule-Based Systems and Sequence Models

Week 10

Advanced Topics in NLP: Transformers and BERT

Week 11

Information Extraction and Named Entity Recognition

Week 12

Project Development: Building and Deploying NLP and AI Systems

PROJECT REPORT

Course Certification

Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of GMU and company handling the course.