What is Boosting in Ensemble Learning

Boosting helps to improve the accuracy of any given machine learning algorithm. It is algorithm independent so we can apply it with any learning algorithms. It is not used to reduce the model variance.
Boosting involves many sequential
iterations to strengthen the model accuracy, hence it becomes computationally costly.

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What is Bagging in Ensemble Learning

Ensemble Learning says, if we can build multiple models then why to select the best one why not top 2, again why not top 3 and why not top 10. Then if you find top 10 deploy all 10 models. And when new data comes, make a prediction from all 10 models and combine the predictions and finally make a joint prediction. This is the key idea of ensemble learning.