  Last years have been characterized by an upsurge of opaque automatic decision
support systems, such as Deep Neural Networks (DNNs). Although they have great
generalization and prediction skills, their functioning does not allow
obtaining detailed explanations of their behaviour. As opaque machine learning
models are increasingly being employed to make important predictions in
critical environments, the danger is to create and use decisions that are not
justifiable or legitimate. Therefore, there is a general agreement on the
importance of endowing machine learning models with explainability. EXplainable
Artificial Intelligence (XAI) techniques can serve to verify and certify model
outputs and enhance them with desirable notions such as trustworthiness,
accountability, transparency and fairness. This guide is meant to be the go-to
handbook for any audience with a computer science background aiming at getting
intuitive insights on machine learning models, accompanied with straight, fast,
and intuitive explanations out of the box. This article aims to fill the lack
of compelling XAI guide by applying XAI techniques in their particular
day-to-day models, datasets and use-cases. Figure 1 acts as a flowchart/map for
the reader and should help him to find the ideal method to use according to his
type of data. In each chapter, the reader will find a description of the
proposed method as well as an example of use on a Biomedical application and a
Python notebook. It can be easily modified in order to be applied to specific
applications.
