Machine learning basics

In this chapter we will introduce the basics of classical machine learning. We will introduce supervised and unsupervised machine learning and process a simple data set. As the number of algorithms used in this field is overwhelming, we just introduce two classical methods: The Random Forest Classifier (supervised) and k-means clustering (unsupervised). With the knowledge acquired while using these two, one can also use other implementations as these algorithms all work similarly, at least from user’s perspective.

To get a wider perspective on available algorithms and methods, the reader is referred to the scikit-learn documentation and Digital Sreeni’s YouTube channel.

Python libraries used in this chapter

We will use scikit-learn which can be installed like this:

mamba install scikit-learn