Statistics - Introduction to Machine Learning in R
13 to 16 May 2025
Royal Statistical Society
Summary
- Certificate of Attendance - Free
- Tutor is available to students
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Overview
This course is being delivered on 13 to 16 May 2025
This course will cover 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 caret library. Participants will be provided with exercises to complete in R 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.
Certificates
Certificate of Attendance
Digital certificate - Included
Description
This courser will cover 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 caret library. Participants will be provided with exercises to complete in R 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
Who is this course for?
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
Requirements
This course assumes participants are comfortable with the basic syntax and data structures in the R language.
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