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, by extracting patterns from the data.
Then, the model is given new data (without the target) about cars and predicts their price (target).
In summary, ML is a process of extracting patterns from data, which is of two types:
- features (information about the object) and
- target (property to predict for unseen objects).
Therefore, new feature values are presented to the model, and it makes predictions from the learned patterns.
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