Creating a Machine Learning model involves two main steps:
Take the example of classifying spam email. Training is where the machine is being taught and trained through a combination of emails and their corresponding spam/not spam labels. The model learns by recognising patterns in features extracted from the data. The model updates its parameters based on how it performs classifying spam emails. The inputs to this stage are the data with annotated labels and the output is a trained model.