This talk aim at presenting a first approaching to the Online Learning methodology. It starts presenting the linear Perceptron algorithm and several derivations. The kernel Perceptron algorithm is also motivated. The Passive- Aggressive (PA) Online Learning algorithm is then presented for binary classification, regression and multi-class problems as well. Linear and kernel version of PA are presented and discussed playing an especial attention to the update rules derivation. An important issue is to be able to control the model complexity, to this end different budgeted online algorithms are proposed, first for the conventional Perceptron and derivations and second, for the PA algorithm. Finally some examples of online learning applications are presented in order to motivate the audience to the application of these techniques in different real scenarios.