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Course Outline
Introduction to Natural Language Processing (NLP)
- Broad overview of NLP and its practical uses
- Core elements: syntax, semantics, and pragmatics
- The specific role NLU plays within the NLP domain
Comprehending NLU Principles
- Defining the scope and meaning of Natural Language Understanding
- Clarifying the distinctions between NLU and NLP
- Essential algorithms employed in NLU
Core NLU Methodologies
- Tokenization and sentence boundary detection
- Identification of named entities (NER)
- Sentiment analysis and categorization of text
Language Modeling in the Context of NLU
- Overview of statistical and neural-based language models
- Analysis of word embeddings and models that account for context
- How language models are applied in NLU workflows
Challenges in NLU
- Managing inherent ambiguity in human language
- Achieving contextual understanding and resolving ambiguity
- Addressing limitations in low-resource languages
Practical Applications of NLU
- Implementing NLU in chatbots and virtual assistants
- Extracting information from unstructured textual data
- Real-world case studies across diverse sectors
Future Developments in NLU
- Progress in deep learning techniques for NLU
- Novel approaches to contextual comprehension
- Forecasting the evolution of human-AI interaction
Recap and Path Forward
Requirements
- Foundational proficiency in programming (specifically Python)
- A keen interest in Artificial Intelligence and language technologies
Target Audience
- Individuals new to AI concepts
- Students specializing in data science
- Technology enthusiasts
14 Hours
