Wikipedia - the online encyclopedia - has long been used as a source of information for researchers, as well as being a subject of research itself [11, 12, 23, 5, 6]. Wikipedia has been shown to be effective in recommender systems, sentiment analysis, validation and multiple domains in information retrieval. One of the reasons for Wikipedia's popularity among researchers and practitioners is the multiple types of information it contains, which enables practitioners to select the right "tool" for their respective tasks. In addition to its great potential, this multitude of information sources also poses a challenge: which sources of information are best suited for a specific problem and how can different types of data be combined? This tutorial aims to provide a holistic view of Wikipedia's different features - text, links, categories, page views, editing history etc. - and explore the different ways they can be utilized in a machine learning framework. By presenting and contrasting the latest works that utilize Wikipedia in multiple domains, this tutorial aims to increase the awareness among researchers and practitioners in these fields to the benefits of utilizing Wikipedia in their respective domains, in particular to the use of multiple sources of information simultaneously.