  Today, intelligent systems that offer artificial intelligence capabilities
often rely on machine learning. Machine learning describes the capacity of
systems to learn from problem-specific training data to automate the process of
analytical model building and solve associated tasks. Deep learning is a
machine learning concept based on artificial neural networks. For many
applications, deep learning models outperform shallow machine learning models
and traditional data analysis approaches. In this article, we summarize the
fundamentals of machine learning and deep learning to generate a broader
understanding of the methodical underpinning of current intelligent systems. In
particular, we provide a conceptual distinction between relevant terms and
concepts, explain the process of automated analytical model building through
machine learning and deep learning, and discuss the challenges that arise when
implementing such intelligent systems in the field of electronic markets and
networked business. These naturally go beyond technological aspects and
highlight issues in human-machine interaction and artificial intelligence
servitization.
