As you know we are living in a world of humans and machine.Humans have been evolving and learning from their past experience for millions of years. On the other hand the era of machine rewards have just begun. You can consider it in a way that currently we are living in the primitive age of machines. While the future of machines is enormous and is beyond our scope of imagination. In today’s world these machines or the robots have to reprogrammed before they start following your instructions. But what if the machine started learning on their own. From their experience walk like us feel like us do things more accurately than us. Might have and start a war of their own. Now these things sounds fascinating and a little scary right. Well let us remember this is just the beginning of the new era.

Now let’s suppose one day you went for shopping apples. The vendor had a card full of apples from where you could hand-pick them. Weigh them and pay them according to their fixed rate. Now the question arises is How will you choose the best apples. You were informed that bright and red apples are sweeter than Peel and the yellow ones. So you make a simple rule pick only from the bright red apples. You check the color of the apples pick the bright yellow ones pair up and return home. Right. Now. When you went home and dated all the apples some of them were not sweet as you thought. You’ve concluded that when it comes to shopping them. You have to look for more than just the colors. After a lot of pondering and tasting different types of apples. You can clear that the bigger and brighter red apples are guaranteed to be sweet. While the smaller bright red apples are sweet only half of the time. The next time at the market you see that your favorite vendor has gone out of town. Now you decide to buy from a different vendor. Who supplies apples grown from a different part of the country. Now you realize that the rule which you had learned that the big red red apples are the sweetest is no longer applicable here. You made another observation here that at this particular vendor that soft apples are the tastiest. Well let’s suppose you go out with a girlfriend and she does not even like apples. You know how girlfriends are, Right?
And she would like you to buy oranges for her. Not all your accumulated knowledge about apples is worthless at this point of time. Now you have to learn everything about the correlation between the physical characteristic and the taste of the oranges. By the same method of experimentation. But then again this is not as difficult as your time. But what if you have to write a code for it. As humans. You would write a code something like this. If apple is bright yellow and the size is big. That implies the apple is sweet. And if the apple is soft that implies the apple is tasty. No conclusion as a human is that every time you make a new observation from your experiment. You have to modify the list of rules manually. You have to understand the details of all the factors affecting the quality of the mouse. If the problem gets complicated enough it might get difficult for you to make accurate rules my hand that goes all the possible types of apples. Not this will take a lot of research and effort and not everyone has this amount of time. So this is where machine learning comes into work.

Well. Machine learning is a concept which allows the machine to learn from examples and experience. And that too without being explicitly programmed. So instead of you writing the code. What you do is feed the reader to the generic algorithm and the algorithm or the machine. Bolster logic based on the given data.

Now let’s have a look at some of the features of machine learning which makes our life much more easier. What it does is that it uses the data to detect patterns in a data set an adjusted program action accordingly. It focuses on the development of the computer programs that can teach them selves to grow. And change when exposed to new data. It enables computers to find hidden insights using iterative algorithms without being explicitly program. So now machine learning plays an important role in our day to day life as well.

You might not know it but you are surrounded by a lot of examples of machine learning. And a lot of which is something that you cannot live without. For example the first one is Google Maps. Not Google Maps is probably the app for use. Whenever you go out. I require assistance in the direction and traffic. Not the other day I was traveling to another city and took the expressway. And the map suggested. Despite the heavy traffic you are on the fastest route. But that was fine for me. But how does it know that. Well it’s a combination of people currently using this service. The historic data of their route collected or what the time. And few tricks acquired from other companies. Not everyone using maps is providing their location. The average speed. The route in which they are traveling which in turn has Google collect massive data about the traffic. Which makes them predict the upcoming traffic and adjust your route according to it.

Now another application is deprived recommendation. You check an item on Amazon but you do not buy it then and there. But the next day you are watching videos on YouTube. And suddenly you see an ad for the same item. You switched to Facebook. Chatting with your friends. And there also you see the same ad. So how does this happen. Well this happens because Google tracks your search history. And recommends ads based on your search history. This is one of the coolest application of machine learning. In fact you won’t believe that 35 percent of Amazon’s revenue is generated just only by product recommendation.

Well here is one of the coolest application on machine learning by far. It is here and people are already using it. And that is the self-driving cars. No machine learning plays an important role in the self-driving car. And I’m sure you guys might have heard about Tesla. The leader in this business.