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Duration 14 hours
Course Outline
Introduction to Natural Language Processing (NLP)
- Brief overview of NLP and its uses
- Core elements: syntax, semantics, and pragmatics
- The position of NLU within the NLP framework
Exploring NLU Principles
- Definition and scope of Natural Language Understanding
- Distinctions between NLU and NLP
- Fundamental algorithms employed in NLU
Core NLU Methods
- Tokenization and sentence segmentation
- Named entity recognition (NER)
- Sentiment analysis and text classification
Language Modeling within NLU
- Overview of statistical and neural language models
- Investigating word embeddings and context-sensitive models
- Usage of language models in NLU workflows
Obstacles in NLU
- Ambiguity inherent in natural language
- Contextual comprehension and resolving ambiguity
- Handling low-resource languages
Real-World Applications of NLU
- NLU in chatbots and virtual assistants
- Extracting information from unstructured text
- Industry-specific case studies
Emerging Trends in NLU
- Progress in deep learning for NLU
- New approaches to contextual understanding
- The future trajectory of human-AI interaction
Recap and Future Directions
Requirements
- Familiarity with programming basics (Python)
- Curiosity regarding AI and language technologies
Target Audience
- Newcomers to AI
- Data science students
- Tech enthusiasts
