Course curriculum

  • 2

    Recorded videos and slides

    • Video 1.1: Modelling continuous explanatory variables with Generalized additive models

    • Slides 1.1: Modelling continuous explanatory variables with Generalized additive models

    • Quiz 1.1

    • Video 1.2: Penalized regression techniques (Lasso, Ridge, interaction detection, etc)

    • Slides 1.2: Penalized regression techniques (Lasso, Ridge, interaction detection, etc.)

    • Quiz 1.2

  • 3

    Interactive e-learning

    • Introduction to machine learning

    • Supervised Machine Learning: Part 1

    • Supervised Machine Learning: Part 2

  • 4

    Case Studies

    • Example: Prediction of Number of claims with a Regression tree

    • Hands-on Case Study: Prediction of Number of claims with a GBM

    • Hands-on Case Study: Prediction of Random Forest on Average Claim Amount

  • 5

    Live Lesson

    • Live Lesson: Cross-validation and parameters tuning: How to calibrate a ML model in practice

    • Slides: Cross-validation and parameters tuning: How to calibrate a ML model in practice

    • Recording: Cross-validation and parameters tuning

  • 6

    Appendix

    • Blank R Notebook

    • Introducing R and Rstudio

    • A User Guide For Jupyter Notebooks

    • RStudio Helpful Guide (Ref 1)

    • Data Importing Helpful Guide (Ref 2)

    • dplyr Helpful Guide (Ref 3 )

    • ggplot Helpful Guide (Ref 4)

    • List of Relevant Resources from the Resource Library

  • 7

    Submissions and solutions

    • Submission: Prediction of Number of claims with a GBM

    • Submission: Prediction of Random Forest on Average Claim Amount