The C4.5 algorithm is a popular method used in machine learning for creating decision trees. It builds upon its predecessor, ID3, by handling both categorical and continuous data. C4.5 works by selecting the attribute that best separates the data into different classes, using a metric called information gain. Once the best attribute is chosen, the algorithm splits the dataset and recursively applies the same process to each subset. This continues until all data is classified or no further splits can improve the model. C4.5 is known for its efficiency and ability to handle missing values, making it widely used in various applications.