Google indicates that there are seven steps for ML:

1. Gathering the data
2. Preparing the data
3. Choosing a model
4. Training
5. Evaluation
6. Hyperparameter tuning
7. Prediction

Let's go through each of the steps with an example. Let's say we are training the model to check whether a piece of fruit is an apple or a lemon. We need to choose the features that we will use to train our model. There are lots of possible alternatives, including shape, color, taste, and skin smoothness:

For this particular training, we will use color and sugar content. The second measurement is probably not the simplest one to obtain, but for this test, let's assume that we have the proper equipment to do so.