Introduction to Machine Learning with R
This is a one day hand-on introduction to Machine Learning with R, covering the all-important concepts of out-of-sample testing, from simple test/train splitting to k-fold cross validation.
Key topics will also include model tuning, model selection and deployment, feature engineering and missing value imputation and interactive visualisation for insights and storytelling.
Please bring laptops with the following installed:
R/Rstudio, tidyverse, xgboost, glmnet, ranger