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Course Outline
Introduction to NLP Methods
- Word and sentence tokenization
- Text classification
- Sentiment analysis
- Spelling correction
- Information extraction
- Parsing
- Meaning extraction
- Question answering
NLP Theory Overview
- Probability
- Statistics
- 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 knowledge of NLP is necessary.
Required: Proficiency in at least one programming language (e.g., Java, Python, PHP, VBA).
Expected: Competent mathematical abilities (A-level standard), particularly in probability, statistics, and calculus.
Beneficial: Familiarity with regular expressions.
21 Hours
