In the past decade, there has been quite a lot of buzz around the word ‘Machine Learning’; but what does Machine learning mean. By definition, Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. However, What does Machine Learning mean in layman’s terms? To simplify it we can say that Machine learning is basically training a software program with examples, experiments, experiences, for it to generate a pattern and make predictions for us in the future and hence complete its goal.
For example, when we play badminton for the first time, we don’t hit the shuttle hard enough or gentle enough, or it’s quite possible that our racket doesn’t touch the shuttle at all. After a few tries, we learn the right angle, direction, position of the racket, the position of the shuttle to get a perfect (or good enough) hit. With more practice and time, we get the experience, we get to know our angles and the amount of strength required to hit the shuttle. Also, we get to know what type of rackets suits us for better accuracy. This is what exactly Machine Learning does.
Over time we get experience by tweaking some parameters and our game improved well incase of machines algorithms are used to train(practice) the historic data(experiences) to achieve your goal (game). The next question that arises is – How exactly does a Machine learn all of this? This is where Mathematics comes into the picture. Machines learn by using complex math concepts.
In Machine Learning we don’t code the the logic for our program instead we want a machine to figure out the logic from the data that we own for us.
But again, why are we using Machine Learning? So, we use Amazon for shopping, Netflix to watch movies, Twitter, Facebook, Instagram are some of the other apps we use to surf through for content and Google is equivalent to oxygen for us to survive today. All these platforms collect our data and provide us with more content or information that we are interested in. The recommended movies block on Netflix are the movies which are quite similar to the ones we watched in the past. Our suggested friends/ followers/ pages/ accounts on Instagram, Facebook, Twitter accounts are based on our own search history or our mutual friends or posts that we have liked in the past. But have we ever wondered how these platforms know what we like, dislike, interested in, etc. It basically runs an algorithm based on the data that we provide while surfing, browsing, shopping, and in exchange give us some more content that we entertain ourselves with. This is the wonder of Machine Learning.
Pranali More
Data Scientist
