Introduction to Machine Learning in R

Organised by

Royal Statistical Society

Presenter

Dr Colin Gillespie or Dr Jamie Owen

Date

19/10/2021 - 20/10/2021

Venue

12 Errol Street/Online

Map

View in Google Maps  (EC1Y 8LX)

Contact

training@rss.org.uk

Description

This is a two day course covering the application of machine-learning methodology to real-world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the tidymodels suite of packages by Rstudio.  Participants will be provided with exercises to complete in R, as well as interactive quizzes so as to gain hands-on experience in using the methods presented. 

The individual stages of: problem formulation, data preparation, feature engineering, model selection and model refinement will be walked through in detail giving participants a solid process to follow for any machine-learning analysis. This includes methods for evaluating machine-learning models in terms of a performance metric as well as assessing bias and variance. 

Delegates are expect to bring a laptop with the R software installed.
 

Learning Outcomes

Following this course the attendees will:

  • Be familiar with the overall process of how to apply machine-learning methods in an analysis project

  • Understand the differences and similarities between statistical modelling and machine-learning theories

  • Have gained hands-on experience in working with the caret package in R

  • Gain an intuitive understanding of how several specific machine-learning methods solve the problems of prediction and classification
                                                    

Topics Covered

  • Introduction to machine-learning: caret package; basic train and test

  • Stages of machine-learning: problem formulation; data preparation; feature engineering; model selection

  • Highlighted Models: Decision trees and random forests; gradient-boosting decision trees; support vector machines
     

Target Audience

Machine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products
 

Assumed Knowledge

This course assumes participants are comfortable with the basic syntax and data structures in the R language.

Level

Intermediate (some prior knowledge)

Cost

£588 - £816 (inc. VAT)

Website and registration

Region

Greater London

Keywords

Quantitative Data Handling and Data Analysis, R , Tidymodels , Machine learning , Problem formulation , Data preparation , Feature engineering , Model selection , Model refinement

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Quantitative Data Handling and Data Analysis

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