As mentioned earlier, machine learning is all about building mathematical models in order to understand data. The learning aspect enters this process when we give a machine learning model the capability to adjust its internal parameters; we can tweak these parameters so that the model explains the data better . In a sense, this can be understood as the model learning from the data. Once the model has learned enough--whatever that means--we can ask it to explain newly observed data.
This process is illustrated in the following figure:
A typical workflow to tackle machine learning problems
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