This lecture provides a practical exploration of classification and regression trees, alongside bootstrap aggregating, in the context of individual reserving in insurance. Beginning with a thorough overview of theoretical foundations often overlooked in introductory materials, we establish mathematical formalities crucial for understanding. Expanding upon prior research, we integrate regression trees and bagging techniques to enhance the accuracy of reserve estimates, particularly in modeling claim sizes. By applying these methods to insurance data, empirical distributions are derived, enabling the calculation of confidence intervals and quantiles necessary for determining reserves for both the upcoming year and ultimate reserves. Through this examination, attendees gain insights into leveraging machine learning for more effective individual reserving strategies within the insurance sector.