Each machine learning model that you make will recognize different categories. For example, you could create an image recognition model to determine whether a picture shows either you or your cat. To create a model, you will use data that are labeled with the correct categories; in our example, the data might be pictures of you or your cat labeled “person” or “cat.” This data is called the training data, and each piece of data is a sample. This type of machine learning, where the training data are labeled with the correct categories, is called supervised learning. There are also algorithms that can determine the categories for themselves (unsupervised learning), but we won’t be using those here.
The training data is used to train the machine learning model. Training is a mathematical process that happens behind the scenes with the Google Teachable Machine. Basically, the computer is trying to find equations that use features in a sample to predict the category it belongs to. For example, if your cat has orange stripes, the model might use color to distinguish between you and the cat.