The seven steps of ML

Google indicates that there are seven steps of 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:

Figure 14.2 – Objects for the ML experiment

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.

Gathering and preparing the data

Let's start gathering data by buying multiple apples and lemons. We will start with the creation of tables with two features...