The concept of ML is depicted with an example of predicting the price of a car. The ML model learns from data, represented as some features such as year, mileage, among others, and the target variable, in this case, the car's price.
Then, the model extracts patterns from the data and predicts the price of cars that were not considered in the training data.
In summary, ML is a process of extracting patterns from data, which is of two types:
- features (information about the object) and
- target (correct predictions).
Therefore, new features are presented to the model, and it makes predictions considering learned information.
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