You will at this point be familiar with different types of machine learning. We will now focus our attention on the supervised learning setting. Throughout this section you will be introduced to different supervised learning problems and familiarize yourself with different types of data. We will also look at the first concrete machine learning method: k-Nearest Neighbors (or k-NN for short). This is an intuitive method for making new predictions based on a set of training data.

These new concepts will be introduced first in the course book and then through two example tasks for you to solve. In the first example you will be asked to build a model for classifying bats and we will then look at an example problem of predicting the price of apartments.

Start by reading chapters 2.1 and 2.2 in the course book. You can then move on to the bat classification task below.

I've read chapters 2.1 and 2.2 in the course book.