1. Don't Be Fooled by the Kernel Trick

Welcome to the first section of the course. Our goal for this section is to start off with a basic linear model and apply the kernel trick to derive an advanced non-parametric analogue.

We shall cover:

• Projecting data into higher dimensions to obtain linear separability
• Using the kernel trick to derive the Kernel Ridge Regression (KRR) model
• Experimenting with KRR models on different data sets
• Identifying and choosing suitable hyperparameters and deploying the model