Before machine learning became mainstream, programmers wrote rules derived from a function of their domain knowledge, observation of some hand-picked instances, and the business requirement to perform a particular task. This concept is very well explained by one of the most highly cited papers in the world of psychology titled "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information." Commonly cited as Miller's law, the paper describes the limited amount of information an average brain can hold and how it becomes unmanageable with the increasing number of variables and dimensions. By now, we understand what type of business problems machine learning algorithms are best suited for and what are the broad categories in terms of statistical formulation of the given use case. No rule book or guide can give you an instant answer, but we will discuss the factors experienced data scientists consider while selecting a set of candidate algorithms.