We provide course about Python & NLP in english. Learn how to write programs that analyze written language. The course will balance theoretical foundations with practical examples using the Python programming language.
Course outline:
Module 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
Module 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
Module 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
Module 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.
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 bot that talks like Shakespeare (or Trump)
Target audience:
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.
Prerequisites:
• No prior experience with libraries such as NLTK or scikit-learn is required for this course
Language:
• English course material, english speaking instructor