Traditional Programming vs. Machine Learning

Traditional Programming vs. Machine Learning

When you learn how to solve problems using algorithms, you receive a set of input data and the expected output. It’s your job to write the specific instructions to turn those inputs into the desired output. The solution is written as steps in a syntax that any computer can understand, called programming languages. The computer’s job, in this case, is to execute each instruction as a recipe. Since the first Machine Learning algorithms appeared, this process has been called traditional programming or traditional approach.

The machine learning approach takes the same inputs and outputs we talked about before and hands them to a machine learning algorithm to find the instructions that convert these inputs into the expected outputs.

In traditional programming, the programmer provides the rules for the computer to follow. In machine learning, the computer is given data and uses algorithms to learn patterns and make decisions based on those patterns. The outcome in machine learning is determined by the data and the algorithms, not by the programmer.

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