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    Machine Learning basics and its real-life examples

    Introduction to Machine Learning

    As you know, we are living in a world of humans and machine humans have been evolving and learning from the past experience for millions of years. On the other hand, the era of machines and robots have just begun. So 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 be programmed before they start following your instructions. But what if the machine started learning on their own from their experience? Work like us, feel like us, do things more accurately than us, might even start a war of their own. Now, these things sound fascinating and a little scary, right? Let us remember, this is just the beginning of the new era.

    Now, let’s suppose one day you went shopping for Mangoes the vendor had a cart full of mangoes from where you could handpick them, weigh them and pay them accordingly with a fixed rate. Now, the question arises is how will you choose the best mangoes? You were informed that bright and yellow mangoes are sweeter than peel and the yellow ones. So you make a simple rule pick only from the bright yellow mangoes. You check the color of the mangoes, pick the bright yellow ones, pay up and return home. Right now, when you went home and tasted all the mangoes, some of them are not as sweet as you thought.

    You concluded that when it comes to shopping for mangoes, you have to look for more than just the colors. After a lot of pondering and tasting different types of mangoes, you concluded that the bigger and bright yellow mangoes are guaranteed to be sweet while the smaller bright yellow mangoes are sweet only half 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 mangoes from a different part of the country. Now you realize that the rule which you had learned that the big red yellow mangoes are the sweetest is no longer applicable here. You made another observation here that at this particular vendor that soft mangoes are the juiciest. But let’s suppose you go out with a girlfriend and she does not even like mangoes. You know your girlfriend is not interested in mangoes and she would like you to buy oranges for her. Now all your accumulated knowledge about mangoes is worthless at this point. 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 you thought. But what if you have to write a code for it? So as humans, you would write a code, something like this.

    If the mango is bright yellow and the size is big, that implies the mango is sweet. And if the mango is soft, that implies a mango is juicy. No conclusion as a human is that every time you make a new observation from your experiments, you have to modify the list of rules manually. You have to understand the details of all the factors affecting the quality of the mangoes. If the problem gets complicated enough, it might get difficult for you to make accurate rules by hand. That goes for all the possible types of mangoes. Now 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 the picture? Well, machine learning is a concept that allows the machine to learn from examples and experience and that do 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 based on 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 easier. So what it does is that it uses the data to detect patterns in a data set and adjust the program action accordingly. It focuses on the development of computer programs that can teach themselves to grow and change. When exposed to new data, it enables computers to find hidden insights using iterative algorithms without being explicitly programmed.

    Role of M.L in our daily life

    Machine Learning

    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. Now, Google Maps is probably the app for use whenever you go out and require assistance in the direction and traffic. Now 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 the subways, the historic data of the route collected over time, and few tricks acquired from other companies. Now 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 deploying 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 switch to Facebook chatting with your friends and then also you see the same ad.

    So how does this happen? Well, this happens because Google tracks your search history. I recommend ads based on your search history. This is one of the coolest applications 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 applications of machine learning by far. It is here and people are already using it, and that is the self-driving cars and 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. And their current artificial intelligence is driven by the hardware manufacturer Invidia, which is based on a type of machine learning, which is the unsupervised learning algorithm. Now, there are certain steps that any machine learning algorithm has to follow. So the first step is data collection, and the stage involves the collection of all the relevant data from various sources. Now, the second step, after collecting all the data is data wrangling, which is the process of cleaning and converting the raw data into a format that allows convenient consumption. Now, after the data have been cleaned and converted into a particular format, the data is analyzed to select and filter the data required to prepare the model. Because not all the data is required for a particular model, you have to select certain features.

    Now, after selecting the features, the algorithm is trained on the training data set through which the algorithm understands the pattern and the rules which go on the data. After this, the testing dataset determines the accuracy of our model. And after this model is ready. So the final stage comes is that the speed and the accuracy of the model are acceptable. Then that model should be applied in the real system. And after the model is deployed based upon its performance, the model is updated and improved. And if there’s a dip in the performance, the model is retrieved.

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