Gradient boosting ensemble technique for regression
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. (source: Wikipedia)
This is a great video tutorial from Alexander Ihler, Associate Professor at Information & Computer Science, UC Irvine.
You can found other interesting data science tutorials made by Alexander Ihler in this YouTube channel: