In this course we will introduce the basic ideas and algorithms of supervised learning and we will implement them using R programming language. A brief theoretical overview of the so-called learning setting will be provided, then the main focus will be on showing practical analysis and modelling of data related to healthcare.
Day 1: Introduction
What is machine learning? Types of machine learning. Classification and regression. Training and test sets. Model evaluation. Over-fitting. Overview of Machine Learning Algorithms. No free lunch theorem. Cross validation. Practical Examples
Day 2: Data Preparation and Feature Engineering
Data analysis and pre-processing, exploratory data analysis, handling missing data. Feature engineering techniques including but not limited to: transformations, feature extraction, reduction and selection.
Day 3: Regression (part 1)
Single and multiple regression. Linear and polynomial regression. Parameter estimates. Residual analysis. Metrics for model evaluation. Plots and predictions. Feature selection.
Day 4: Classification
Day 5: Regression (part 2)
Using decision trees and random forests for regression. Introduction to regularisation (Ridge, LASSO and Elastic Nets).
Your training will be led by:
To do this course you will need to be comfortable with using base R and the tidyverse.
This course is free and available to all those working in the Midlands Health and Social Care sector , e.g. NHS, Public Health, Local Authority, ICBs
Five days (09:30-15:30)
Online – delivered via Zoom with a combination of delivery styles.
Registration: Please use the online link below