The notion of machine learning is truly omnipresent in these days. In this web session we thus give participants an introduction to this interesting field and show machine learning techniques. We learn how we are able to implement a workflow and give an overview of several methods such as random forest, gradient boosting, and neural networks. A vital step of machine learning algorithms is the development and training phase of models, in particular, in order to build up powerful applications. Do machine learning models have to be black box applications or are we able to bring light into the darkness? This is a vital question from a practical point of view, which we answer in this web session. Companies are concentrating more and more on using machine learning for several applications, and, therefore, it is important to have a closer look at the current regulatory requirements.
By focusing on all these aspects of machine learning, we give practical examples from the insurance and banking industry: we provide Python codes such that participants directly see how machine learning workflows can be implemented.
