GitHub
LinkedIn
Twitter
YouTube
RSS

Training Course Details

Machine Learning with Tidymodels

Machine Learning with Tidymodels

Course Level: Intermediate

Machine learning is the process of applying statistical techniques to gain systematic information about a quantity of interest. We will be specifically focusing on how we can use the {tidymodels} suite of packages to implement these techniques. We cover key reasons for model fitting, such as predicition and inference, on quantitative and qualitative responses.

No Events Currently Scheduled

Sorry, there are no upcoming events for this course, but please get in touch if you would like to be kept informed when events are scheduled in the future.

View our full training course calendar »

Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • Introduction: A general introduction into statistical moedlling techniques and the {tidymodels} R package.
  • Quantitaive model fitting: Using {parsnip} to perform simple and multiple linear regression, allowing us to fit a model.
  • Qualitative model fitting: Using {parsnip} to perform logistic regression and fit the model.
  • Classification: Brief introduction to the K-nearest neighbours (KNN) technqiue for cases where a categoric response has more than two possible outcomes.
  • Model assessment: Testing the reliability of model predictions and finding optimum models using resampling methods like the validation set approach.
  • Model pre-processing with {recipes}: Including variable trasnformations, one-hot encoding and normalisation

Learning Outcomes

By the end of the course participants will…

  • have a thorough understanding of popular analytical techniques practised in industry today
  • be able to make model predictions in cases of a numeric or categoric response
  • assess model validity through the validation set approach
  • understand which technique applies to their own data
  • efficiently and effectively analyse their own data using said techniques in R
  • apply the developed workflow practice to other modelling techniques
  • pre-process their own data using {recipes}

Materials

  • Example course material for 'Machine Learning with Tidymodels
  • Example course material for 'Machine Learning with Tidymodels
  • Example course material for 'Machine Learning with Tidymodels
  • Example course material for 'Machine Learning with Tidymodels
  • Example course material for 'Machine Learning with Tidymodels

Prior Knowledge

It will be assumed that participants are familiar with R. For example inputting data, basic visualisation, basic data structures and use of functions. Attending the Introduction to R course will provide a sufficient background, but the programming with R will be helpful.

Attendee Feedback