This virtual tutor led 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 is 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.

Theo Roe

Theo holds a 1st Class Honours MMathStat in Mathematics & Statistics from Newcastle University. He is the author of many of the Jumping Rivers courses and works with a range of clients