Natural Language Processing with Real-World Project Implementation

This course is designed for individuals interested in Natural Language Processing (NLP), regardless of their background. Whether you’re a beginner looking to grasp the basics or an experienced professional aiming to enhance your NLP skills, this course provides a structured learning path. Dive into the world of NLP, develop hands-on expertise, and bring your knowledge to life with a practical chatbot project.

The course will allow you to explore the methods of Natural Language Processing (NLP), from fundamental linguistic concepts to advanced techniques like sentiment analysis and named entity recognition, this hands-on program equips you to build practical NLP applications. Cap off your learning journey by creating an application in the final project, merging theoretical knowledge with real-world implementation. 

• Basics of Programming in Python

Module 1: Introduction to NLP Fundamentals

• Understanding the basics of Natural Language Processing (NLP)
• Exploring linguistic concepts and their relevance to NLP
• Introduction to tokenization, stemming, and lemmatization

Module 2: Advanced NLP Techniques

• In-depth exploration of advanced techniques such as sentiment analysis
• Named Entity Recognition (NER) and its applications
• Leveraging machine learning for text classification and clustering

Module 3: Practical NLP Applications

• Building practical applications using NLP
• Text summarization and keyword extraction
• Extracting insights from unstructured data through NLP

Module 4: Bridging Theory and Real-World Implementation

• Understanding the importance of merging theoretical knowledge with practical implementation
• Case studies of successful NLP applications in various industries
• Identifying challenges and solutions in real-world NLP projects

Module 5: Final Project – Application Development

• Applying acquired knowledge to develop a complete NLP application
• Selecting a project scope and defining objectives
• Hands-on implementation of NLP techniques in the application

Module 6: Evaluation and Optimization

• Assessing the performance of the developed NLP application
• Identifying areas for optimization and enhancement
• Iterative improvement for enhanced functionality

Module 7: Ethical Considerations in NLP

• Addressing ethical considerations in NLP projects
• Privacy, bias, and responsible use of NLP technologies
• Ensuring compliance with ethical standards in application development