Google indicates that there are seven steps for ML:
- Gathering the data
- Preparing the data
- Choosing a model
- Training
- Evaluation
- Hyperparameter tuning
- 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 ...
