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Python & NLP training course

Learn how to write programs that analyze written language

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Highlights Overview Audience Course Details Prices & Dates
JBI training course London UK
  • Process text data using Python tools
  • Extract important keywords and topics of interest from large text collections
  • Capture semantics using efficient text representations like word embeddings
  • Use Machine Learning techniques to categorise documents
  • Learn about the latest approaches to tackle advanced applications like language generation, entity recognition and text summarisation

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The course will balance theoretical foundations with practical examples using the Python programming language.

No prior experience with libraries such as NLTK or scikit-learn is required for this course.

Having existing experience with Python will be extremely beneficial but not required: users of other programming languages and tools (including e.g. Java, C++, C#, JavaScript, Matlab, Excel or Rlang) will find this course beneficial.


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JBI training course London UK
JBI training course London UK

Software engineers, data scientists, data analysts, researchers and students who want to get started with Natural Language Processing applications, with the purpose of extracting useful information from free-text data.


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Course Details

1 - Foundations

This first section provides the basic tools and techniques to get started with Natural Language Processing

Overview on NLP applications and the Python

  • NLTK
  • spaCy
  • Gensim
  • scikit-learn

Working with text

  • Tokenisation
  • Text pre-processing
  • Regular Expressions

Word frequencies and co-occurrences

  • Stop-words and Zipf's Law
  • Mining topics of interest with co-occurrences

Text Representation

  • n-grams
  • Bag-of-words
  • Word embeddings and document embeddings

 

2 - Topic Modelling

This section aims at improving our understanding of a document, or a collection of documents, using techniques that go beyond simple word frequencies.

Topic Modelling

  • Bird's-eye view on a document or a dataset
  • Navigating topics and sub-topics in a document or a dataset

 

3 - Text Classification

This section tackles the problem of classifying documents into a set of predefined categories.

 

  • Categorising documents
  • Topic Classification
  • Sentiment Analysis
  • Model evaluation: assessing classification quality
  • Model introspection: explaining the classification results

4 - Overview on Advanced Applications

The last section offers an outlook on advanced NLP problems, so delegates are equipped with ideas and techniques to tackle more specific applications

Search Engines

  • Building a search engine to retrieve relevant documents from a custom data set of text

Named Entity Recognition

  • Identifying named entity in text

Text Summarisation

  • Extracting the most useful sentences from a document or a collection of documents 

Natural Language Generation

  • Creating an AI conversational agent for customer service and similar
 
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