Introduction to Machine Learning in R - Virtual Classroom

Date: Tuesday 20 October 2020 9.30AM - Wednesday 21 October 2020 5.00PM
Location: Online
CPD: 12.0 hours
RSS Training
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This is a two day virtual tutor-lead 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. 

 

This is a two day virtual tutor-lead 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. 


 

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
     

Assumed Knowledge

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

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

 
The course tutor will be one of the following:

Dr Colin Gillespie

Dr Colin Gillespieis a statistics lecturer at Newcastle University and is often employed as an R consultant by Jumping Rivers. He has been using R since 1999 and teaching R programming for the last eight years. Colin has authored a number of R packages and regularly answers R questions and is a top contributor on stackoverflow.

Dr Jamie Owen

Dr Jamie Owen has a PhD in Statistics, with a focus on scalable Bayesian inference. Since leaving academia, he has applied these skills in a number of commercial settings. His focus is using complex data sets, to give meaningful insights. 

Additionally, he has delivered R training courses ranging from introductory to advanced programming and big data analytics at a wide range of companies and academic institutions in both the UK and Europe, including the Francis Crick Institute and the MoD.
 

Fees

   

Registration before
 20 September 2020

 

Registration on/after
 20 September 2020

                                  

Non Member 

RSS Fellow 

RSS CStat/Gradstat also MIS & FIS

 

£611.00+vat 

£520.00+vat 

£490.00+vat

£680.00+vat 

£577.00+vat 

£543.00+vat

 
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