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Course Outline
Short Introduction to NLP methods
- word and sentence tokenization
- text classification
- sentiment analysis
- spelling correction
- information extraction
- parsing
- meaning extraction
- question answering
Overview of NLP theory
- 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 background in NLP is required.
Required: Familiarity with any programming language (Java, Python, PHP, VBA, etc...).
Expected: Reasonable maths skills (A-level standard), especially in probability, statistics and calculus.
Beneficial: Familiarity with regular expressions.
Testimonials
I did like the exercises
Office for National Statistics
Very knowledgeable
Usama Adam - TWPI
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
This is one of the best quality online trainings I have ever taken in my 13 year career. Keep up the great work!
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.
- Accenture Inc
the last day. generation part
- Accenture Inc
I like that it focuses more on the how-to of the different text summarization methods
About face area.
- 中移物联网
This is one of the best quality online trainings I have ever taken in my 13 year career. Keep up the great work!
I like that it focuses more on the how-to of the different text summarization methods
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