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Course Outline
Introduction to Natural Language Processing (NLP)
- Overview of NLP and its diverse applications
- Core elements: syntax, semantics, and pragmatics
- Positioning NLU within the NLP framework
Grasping NLU Concepts
- Defining the scope and definition of Natural Language Understanding
- Clarifying the differences between NLU and NLP
- Overview of fundamental algorithms utilized in NLU
Foundational NLU Techniques
- Tokenization and splitting sentences
- Identifying Named Entity Recognition (NER)
- Analyzing sentiment and classifying text
Language Modeling within NLU
- Introduction to statistical and neural language models
- Examining word embeddings and models that consider context
- Applying language models to various NLU tasks
Challenges in NLU
- Addressing ambiguity in natural language
- Navigating contextual understanding and disambiguation
- Working with low-resource languages
Practical Applications of NLU
- Integrating NLU into chatbots and virtual assistants
- Extracting information from unstructured text
- Reviewing case studies from multiple industries
Future Directions in NLU
- Progress in deep learning applied to NLU
- New methods in understanding context
- The evolving landscape of human-AI interaction
Conclusion and Next Steps
Requirements
- Familiarity with programming basics (specifically Python)
- A genuine interest in Artificial Intelligence and language technologies
Target Audience
- Individuals new to AI
- Students pursuing Data Science
- Tech-savvy enthusiasts
14 Hours
