What is the difference between Bagging and Boosting?

Bagging and Boosting are similar in that they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, let's start from the beginning: Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers, where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. The second group of multiclassifiers contain the hybrid methods. They use a set of learners too, but they can be trained using different learning techniques.