Online education is one of the wealthiest industries in the world. The relevance of this sector has increased due to the COVID-19 emergency, forcing nations to convert their education systems towards online environments quickly. Despite the benefits of distance learning, students enrolled in online degree programs have a higher chance of dropping out than those attending a conventional classroom environment. Being able to detect student withdrawals early is fundamental to build the next generation learning environment. In machine learning, this is known as the student dropout prediction (SDP) problem. In this tutorial, intermediate-level academicians, industry practitioners, and institutional officers will learn existing works and current progress within this particular domain. We provide a mathematical formalisation to the SDP problem, and we discuss in a comprehensive review the most useful aspects to consider for this specific domain: definition of the prediction problem, input modelling, adopted prediction technique, evaluation framework, standard benchmark datasets, and privacy concerns.