A Support Vector Machine (SVM) is a machine learning method that helps divide data into categories. Imagine drawing a line (or boundary) on a graph to separate different groups of points, like cats and dogs. SVM finds the best line that keeps the groups as far apart as possible. For trickier data, it can use special math (called kernels) to draw curves or work in higher dimensions.

A Decision Tree is a visual and intuitive machine learning method that makes decisions by asking questions about the data. It works like a flowchart, starting with a question at the top and branching out based on answers. These questions are not just yes/no, but can also be comparisons, like "Is the age greater than 18?" or "Is the temperature below 30°C?" Each split is chosen using a quantitative measure, such as information gain or Gini impurity, to find the best threshold for separating the data.