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Course Outline
Introduction to Natural Language Processing (NLP)
- Overview of NLP and its practical applications
- Key components: syntax, semantics, and pragmatics
- The role of NLU within the field of NLP
Understanding NLU Concepts
- Definition and scope of Natural Language Understanding
- Differences between NLU and NLP
- Basic algorithms utilised in NLU
Basic NLU Techniques
- Tokenization and sentence segmentation
- Named entity recognition (NER)
- Sentiment analysis and text classification
Language Modeling in NLU
- Introduction to statistical and neural language models
- Exploring word embeddings and context-aware models
- Applications of language models in NLU tasks
Challenges in NLU
- Ambiguity inherent in natural language
- Contextual understanding and disambiguation
- Addressing challenges with low-resource languages
Applications of NLU
- NLU in chatbots and virtual assistants
- Information extraction from unstructured text
- Case studies across various industries
Future Trends in NLU
- Advancements in deep learning for NLU
- Emerging techniques in contextual understanding
- The future of human-AI communication
Summary and Next Steps
Requirements
- Fundamental knowledge of programming (specifically Python)
- A keen interest in artificial intelligence and language technologies
Target Audience
- Beginners in AI
- Students pursuing data science
- Tech enthusiasts
14 Hours
