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Course Outline
Introduction to NLP Techniques
- 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
- Maxent classifiers
- Sequence models (Hidden Markov Models)
- Probabilistic dependency
- Constituent parsing
- Vector-space models of meaning
Requirements
No prior knowledge of NLP is necessary.
Required: Proficiency in any programming language (such as Java, Python, PHP, or VBA).
Expected: Solid mathematical foundation (A-level equivalent), particularly in probability, statistics, and calculus.
Beneficial: Familiarity with regular expressions.
21 Hours
