Introduction to Machine Learning in R - Virtual Classroom

Date: Tuesday 29 October 2024 1.00PM - Friday 01 November 2024 5.00PM
Location: Online
CPD: 12.0 hours
RSS Training
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Level: Intermediate (I)


This course covers the fundamentals of machine learning and the methodology for applying these 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. Participants will be provided with exercises to complete through the course in order to gain hands-on experience in using the methods presented.

Please note: Bookings will close 4 working days before the course start date or when the course has reached its maximum capacity.

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This course has the Society's Quality Mark so can be used as part of your application for professional membership including Data Analyst.

 
Level: Intermediate (I)


This course covers the fundamentals of machine learning and the methodology for applying these 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. Participants will be provided with exercises to complete through the course in order 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 tidymodels suite of packages 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: parsnip package; basic train and test

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

  • Highlighted Models: Decision trees and random forests; K-nearest neighbours, linear regression and logistic regression.
     

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

For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials.

 

Dr Astrid Radermacher

Astrid did a PhD in Molecular Biology, during which she fell in love with R. She loves extracting meaningful insights from data, and displaying these in visually appealing (and often interactive!) ways.


Dr Keith Newman

Following a PhD in statistics at Newcastle University, Keith developed software to improve road safety modelling. He enjoys creating Shiny apps and teaching the use of R.
 

Fees

   

Registration before 
10 September 2024

 

Registration on/after
10 September 2024

                                  


Non Member 

RSS Fellow 

RSS CStat/Gradstat/Data Analyst 
also MIS & FIS

 

£694.00+vat 

£590.00+vat 

£557.00+vat

£772.00+vat 

£655.00+vat 

£616.00+vat

Group discounts are also available*:


3-5 people

6-8 people

9+ people
*Discount only applies to non-member price

 


10% discount

15% discount

20% discount

 
 
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