Making Sense of Human Language
Learn how to apply statistical learning and language processing techniques to build machine learning models capable of deriving actionable insights from human language and thus enabling automated and contextual interactions between computers and humans.
This course provides a hands-on and in-depth exploration of the industry-standard Python NLTK natural language toolkit, in combination with the Python Scikit-Learn machine learning library, with which to build natural language processing pipelines and machine learning models designed to analyze and derive meaning from text. This course explores in both theoretical and applied detail the major techniques used in natural language processing including text pre-processing, indexing, searching, categorisation, tagging, clustering, entity recognition, entity relationship recognition, word embeddings, feature detectors and custom grammars. Using these techniques, experienced senior data scientists can build digital systems capable of deriving actionable insights from human language with subsequent applications including automating contextual interactions between computers and humans via chatbots and question-answering systems.