This course covers the fundamental topics in machine learning and prepares the audience for more advanced topics.Read more on Fundamental theories in Machine Learning
In this course you learn about exploratory analysis of text data, introduced to sentiment analysis of texts using sentiment lexicons and the concept of topic modelling (package topicmodels).Read more on Natural Language Processing with R
Large volumes of unstructured, free-text data such as patent applications, contain potentially valuable information for policymakers. Here we describe the pipeline used to process such data sources.Read more on pyGrams: An open source tool for discovering emerging terminology in large text datasets
In this report, we focus primarily on how the pyGrams tool can be used to analyse terms through time using the time stamps in document metadata.Read more on Discovering emerging important terminology in large text datasets using pyGrams: a comparison between net growth and e-score methods
We introduce pyGrams – a new Python tool for extracting, visualising and identifying emerging terms in large document collections, such as patents.Read more on Extracting, visualising and identifying emerging important terminology from patent collections
Our latest project investigates the use of machine learning techniques to predict missing energy performance scores. It also attempts to create a complete picture of the energy efficiency profile for domestic properties in Wales.Read more on Can machine learning be used to predict energy performance scores?