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Course Outline
Introduction to NLP Techniques
- Word and sentence tokenization
- Text classification
- Sentiment analysis
- Spelling correction
- Information extraction
- Syntactic parsing
- Semantic extraction
- Question answering
Core NLP Theories
- Probability theory
- Statistical methods
- Machine learning
- N-gram language modeling
- Naive Bayes
- Maximum entropy classifiers
- Sequence models (Hidden Markov Models)
- Probabilistic dependencies
- Constituent parsing
- Vector-space models of meaning
Requirements
No prior background in NLP is necessary.
Required: Familiarity with at least one programming language (e.g., Java, Python, PHP, VBA).
Expected: Solid mathematical skills, comparable to A-level standard, particularly in probability, statistics, and calculus.
Beneficial: Knowledge of regular expressions.
21 Hours
