First off, Machine Learning ≠ Algorithms. There is a general misconception that machine learning is about mastering dozens of algorithms. However, it is much more than that. Machine learning is a comprehensive approach to solving problems. Individual algorithms are only one piece of the puzzle. The rest of the puzzle is how you apply them the right way.
Machine learning is the practice of teaching computers how to learn patterns from data, often for making decisions or predictions. For true machine learning, the computer must be able to learn patterns that it’s not explicitly programmed to identify.
Let’s take the example of a curious child.
A young child is playing at home… And he sees a candle! He cautiously waddles over.
Out of curiosity, he sticks his hand over the candle flame.
“Ouch!,” he yells, as he yanks his hand back.
“Hmm… that red and bright thing really hurts!”
Ooh, a candle!
Two days later, he’s playing in the kitchen… And he sees a stove-top! Again, he cautiously waddles over.
He’s curious again, and he’s thinking about sticking his hand over it.
Suddenly, he notices that it’s red and bright!
“Ahh…” he thinks to himself, “not today!”
He remembers that red and bright means pain and he ignores the stovetop.
To be clear, it’s only machine learning because the child learned patterns from the candle.
He learned that the pattern of “red and bright means pain.” On the other hand, if he ignored the stove-top simply because his parents warned him, that’d be “explicit programming” instead of machine learning.