The field of Machine Learning can be divided into classification and regression problems, which can be either supervised or unsupervised. In this training you will learn how to solve the different problem classes with the most important methods of machine learning.
First, you learn how to use machine learning techniques for monitored classification and regression scenarios. Each of the procedures will be theoretically introduced in the training and then directly applied in R by using small data sets. ‘Real World’ questions, which are formulated on the basis of sample data sets, serve the course as a common thread through the procedures. In addition to the algorithms, the training gives an impression of machine learning processes. This means that it will be shown what steps are necessary to solve a machine learning task and how they are implemented in concrete terms.
It then discusses unmonitored scenarios. These include cluster analysis and principal component analysis. Both process classes are designed to reduce dimensionality in data sets and are often used in interaction with supervised learning methods. You will learn in the training how the procedures are combined and what challenges you encounter while applying them.
