Python & NLP training course

Learn how to write programs that analyze written language

NEXT COURSE 23 January (3 days £1995 + VAT) BOOK NOW

JBI training course London UK

  • Use best practices to build and deploy Machine Learning products using Python
  • Get familiar with Simple Text Manipulation to Search, Count and Split words
  • Process complex structures to Index list, distribute and compare word
  • Acquire knowledge in Natural Language
  • Gain skills in Machine translations (statistical, rule based, literal, etc..)
  • Learn Natural Language Processing with NLP in Python examples
  • Access Text Corpora and Lexical Resources
  • Utilise Processing Raw Text
  • Understand how to Categorize and how to Tag Words
  • Explore Text Classification (Machine Learning)
  • Extract Information from Text: chunking and chinking
  • Learn Analyzing Sentence Structure
  • Build Feature Based Grammars
  • Analyze the Meaning of Sentences with Semantics and Logic
  • Manage Linguistic Data, Data formats and Metadata 


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.

JBI training course London UK
JBI training course London UK

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:



Related Courses

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
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