[AI Explained] How a machine learns?

Machine learning algorithms can be thought of as a set of instructions for a computer to follow, just like a recipe. But instead of telling the computer exactly what to do step by step, we give it a goal and some data, and it tries to figure out the steps to reach that goal on its own.

This process of the computer figuring out the steps on its own is what we mean when we say "a machine is learning."

Here's an analogy to help explain it better: Let's say you want to teach your friend to bake a cake. You could give them a recipe with precise instructions, like "Mix 2 cups of flour, 1 cup of sugar, 1/2 cup of butter, etc."

But this method is inflexible, and your friend wouldn't know how to make the cake if you changed the recipe or ingredients.

Alternatively, you could give your friend the goal of "baking a cake that tastes good," and show them a bunch of cake recipes and ingredients. Your friend would then use their own judgment and experimentation to figure out how to make a cake that tastes good, using the recipes and ingredients as a starting point.

This is similar to how a machine learning algorithm works, where the computer is given a goal and data, and it figures out the steps to reach that goal on its own.

So, the basic idea behind machine learning is that instead of explicitly programming a computer to perform a task, we train it to learn how to do the task by itself, by giving it examples and allowing it to make predictions based on those examples. The computer then continues to improve its performance by learning from its mistakes and adjusting its algorithms accordingly.

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