Content
- Getting started with R
- Regression Models
- linear regression
- multiple linear regression
- performance of regression models
- Classification Models
- logistic regression
- performance of binary classification models
- Learning Machines (AIML)
- supervised learning
- Decision Tree
- Random Forest
- Support Vector Machine
- unsupervised learning
Course Materials
- the code that we use on this workshop: R-code overview
- Basics of R and statistics:
- Data import:
- slides based on part II of the
book
- R-code from those slides
- Data wrangling:
- Building Powerful Models:
- Credit Model (from the sample materials for the book): R
Markdown examples – locate the credit model example
- Understanding Large Language Models: slides