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Python & NLP Training Course

Learn how to write programs that analyze written language

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8 Aug London
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Python & NLP training course (code: PYTHONNLP)

TRAINING COURSE OVERVIEW

This course provides advanced-level training on Machine Learning applications developed with Python.

Delegates will learn best practices for building and deploying Machine Learning products using Python and its rich ecosystem for scientific computing.

The course is intensive and intended for software developers and software engineers with a working knowledge of Python, who want to improve their proficiency in building data products.

The course should be appealing also to Data Scientists and Data Analysts with a basic knowledge of Python. With practical exercises and interactive discussions, the attendees have the opportunity to apply the proposed concepts on real Data Science applications, building predictive analytics software.

AUDIENCE

This course is intended for a diverse range of people who want to learn how to write programs that analyze written language, regardless of previous programming experience:

 


CONTENT

DETAILHIGHLIGHTS

Simple Text Manipulation

  • Searching Text
  • Counting Words
  • Splitting Texts into Words
  • Lexical dispersion

Processing complex structures

  • Representing text in Lists
  • Indexing Lists
  • Collocations
  • Bigrams
  • Frequency Distributions
  • Conditionals with Words
  • Comparing Words (startswith, endswith, islower, isalpha, etc...)

Natural Language Understanding

  • Word Sense Disambiguation
  • Pronoun Resolution

Machine translations (statistical, rule based, literal, etc...)

NLP in Python in examples

Accessing Text Corpora and Lexical Resources

  • Common sources for corpora
  • Conditional Frequency Distributions
  • Counting Words by Genre
  • Creating own corpus
  • Pronouncing Dictionary
  • Shoebox and Toolbox Lexicons
  • Senses and Synonyms
  • Hierarchies
  • Lexical Relations: Meronyms, Holonyms
  • Semantic Similarity

Processing Raw Text

  • Printing
  • Truncating
  • Extracting parts of string
  • Accessing individual characters
  • Searching, replacing, splitting, joining, indexing, etc...
  • Using regular expressions
  • Detecting word patterns
  • Stemming
  • Tokenization
  • Normalization of text
  • Word Segmentation (especially in Chinese)

Categorizing and Tagging Words

  • Tagged Corpora
  • Tagged Tokens
  • Part-of-Speech Tagset
  • Python Dictionaries
  • Words to Propertieis mapping
  • Automatic Tagging
  • Determining the Category of a Word (Morphological, Syntactic, Semantic)

Text Classification (Machine Learning)

  • Supervised Classification
  • Sentence Segmentation
  • Cross Validation
  • Decision Trees

Extracting Information from Text

  • Chunking
  • Chinking
  • Tags vs Trees

Analyzing Sentence Structure

  • Context Free Grammar
  • Parsers

Building Feature Based Grammars

  • Grammatical Features
  • Processing Feature Structures

Analyzing the Meaning of Sentences

  • Semantics and Logic
  • Propositional Logic
  • First-Order Logic
  • Discourse Semantics

 Managing Linguistic Data 

  • Data Formats (Lexicon vs Text)
  • Metadata
  • Simple Text Manipulation
  • Processing complex structures
  • Natural Language Understanding
  • Machine translations (statistical, rule based, literal, etc...)
  • NLP in Python in examples
  • Accessing Text Corpora and Lexical Resources
  • Processing Raw Text
  • Categorizing and Tagging Words
  • Text Classification (Machine Learning)
  • Extracting Information from Text
  • Analyzing Sentence Structure
  • Building Feature Based Grammars
  • Analyzing the Meaning of Sentences
  •  Managing Linguistic Data 

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LONDON COURSES
- INCLUDES LUNCH -

8th Aug 2019 - 3 days £1995
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