This article will help you understand what is Machine Learning (ML) and why it is called Machine Learning, and how it resembles with how other living creatures learn and evolve right from their birth. Let’s see below:
Machine Learning is an art of teaching computers or letting them learn patterns from the data. This is often done because it helps in making informed decisions and sometimes accurate predictions. I promise that’ all the definition you’ll have in this article.
Always remember, if you can hit upon an analogy of what the unknown concept is like, you are half way there. The rest is to practice explaining to a 6 year old. So let’s take an easy example to understand ML:
- Learning the pattern
Let’s say a baby panda while playing in the forest sees a burning bright sun for the first time. Mersmerized, he continues to gaze at the sun. After a few seconds he realizes he is in pain and looks down. So now his mind learns that something that is “Orange and Bright” hurts and burns his eyes.
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2. Remembering and recalling the pattern
A few days later, that same baby panda comes across a light bulb for the first time. Again he is curious. While he is about to gaze at this light he suddenly remembers that orange and bright means pain and will hurt his eyes. Thus he stops staring at the light and looks away. In simple terms, this is Machine Learning since the panda learned a pattern from its past experiences.
But note that, orange and bright pattern might not always mean pain. What happens when the panda sees an Orange? Sure, his instincts will tell him to stay away, but some curiosity and courage will lead him to learning a new pattern. Say, “Orange and Bright but not hot” means food.
In real world you can always re-train and updated your machine learning model as you collect new data. And as you collect more and more data, the ML model is able to learn new patterns in the data and can generate better insights.
Similar to the baby panda example, our instincts and our knowledge of things and surroundings is the result of years of implicit and explicit observations. For e.g. if the baby panda was to be warned by his mother against staring at the sun, that would be explicit programming not machine learning.
So when do we use machine learning?
Sure, choosing between what food to eat, what movie to watch or making calculations like 2+2 is easy and can be done by the human brain. Sometimes, it is not so easy, like deciding on whether to buy a condo or a house, or calculating the result of 143 x 221. Then there are decisions like which stock to buy or identifying where the next terror attack would take place. These kind of decision making needs analysis and requires complex tools to deal with collecting and processing huge amounts of data. Machine learning operates in this category of decision making which requires processing and analyzing large volumes of data.
Thank you for reading.