When engineers work on normal programming like the case of embedded programming, the program shall have the set of statements (rules) and data and the program works as per the rules.
Machine learning models differ from normal programming. Machine learning takes the input data and outputs and defines the rules. In this case, we call Machine Learning models as trained models. For trained models, input data are provided so that the model knows well before what kind of data to expect. For example, if we take the case of autonomous vehicle, the major requirement is to get to know the routes and understand the patterns on the road to drive safely.
For a beginner, one has to understand that it is all the algorithms that work in the background and the researchers come up new algorithms all the time. Machine Learning engineers use these algorithms in their programs to train and get the desired output.
Here is how output is predicted using the linear regression model:
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