In the past few years, there has been much work on incorporating fairness requirements into the design of algorithmic rankers, with contributions from the data management, algorithms, information retrieval, and recommender systems communities. In this tutorial, we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. During the first part of the tutorial, we present a classification framework for fairness-enhancing interventions, along which we will then relate the technical methods. This framework allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. Next, we discuss fairness in score-based ranking and in supervised learning-to-rank. We conclude with recommendations for practitioners, to help them select a fair ranking method based on the requirements of their specific application domain.