Introduction to Machine Learning in R
07/03/2023 - 10/03/2023
Royal Statistical Society
Intermediate (some prior knowledge)
View in Google Maps (EC1Y 8LZ)
Level: Intermediate (I)
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 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 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.
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
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.
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
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.
From £629.75 to £873.94 (including VAT)
Website and registration:
Quantitative Data Handling and Data Analysis, ICT and Software, R, Machine Learning, Tidymodels, Decision trees,
Related publications and presentations:
Quantitative Data Handling and Data Analysis
ICT and Software