General ML Notes

Resources

Model Building

1. Dummy Variables
2. Why it's necessary to create dummy variables 
3. When to Normalise date and when to standardise? 
4. Scaling techniques

Model Comparison (R-squared normalisation techniques)

1. AIC (Akaike Information Criterion)
2. BIC (Bayesian Information Criterion)
3. Mallow's Cp

Feature Selection

1. univariate methods
2. linear models and regularization
3. random forests
4. stability selection, rfe and the above compared