28 February - 2 March 2018
The powerful statistical capabilities of R programming language include a large selection of machine learning algorithms which can be applied to classification, clustering and predictive analytics. This course explores practical applications of the most frequently used machine learning methods such a k-Nearest Neighbours, Naive Bayes, Regressions and Decision Trees algorithms through R statistical environment. It also provides a good introduction to more advanced techniques e.g. Artificial Neural Networks and Support Vector Machines. The course aims to achieve the following goals:
The course will be presented by Simon Walkowiak - an author of "Big Data Analytics with R" and Mind Project's expert in Big Data architecture for predictive modelling. Simon has delivered numerous "Big Data Methods in R" training courses at various institutions, financial/business organisations, governmental departments and UK universities (including Big Data & Analytics Summer School organised by the Institute for Analytics and Data Science). He is also a former Data Curator at the UK Data Archive - the largest socio-economic digital data depository in Europe.