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Machine Learning

Why is machine learning so important and where it is used?

Machine Learning: Future of Tomorrow

The need for inventing new machines has fascinated humans since ancient times. The new inventions are electricity, the wheel, or the internet has resulted in a momentum that has revolutionized the lives of common men. With the invention of computers, the concept of new machines came into existence that could think like them, do things, and even guide them whenever necessary. The search ends up with the concept now named Artificial Intelligence. So, what exactly is Artificial Intelligence or AI?

It is basically the science and engineering of making intelligent machines, especially the applications which could use their intelligence in solving complex problems within seconds which humans can’t. Let me give you an example, suppose ISRO, the premier institute which holds the responsibility of all space-related affairs in India decides to send a Rocket to the Moon. When the rocket gets launched from the Sriharikota and is moving at an alarming speed, then the supercomputer will guide the Scientists to examine at what point the rocket should be turned and to what extent so that it could reach its destination appropriately. Here, the supercomputer uses the concept of AI which helps him to take appropriate action in real scenarios.

Let me introduce you to another example. Suppose you want to know whether it will rain tomorrow or not. If you ask your friend, he might guess it wrong, but if you ask this particular question to a machine, there is probably a chance of getting an answer which could predict it right. This is where the machine uses artificial intelligence but in another manner. Its applications are not just limited to determining if it would rain today or not but have spread its wings to each and every sector whether it is health care, Corporate, Finance, and even in Defence.

In this example, the machine uses Artificial Intelligence’s concept termed Machine Learning. It is basically the method of analyzing the data that encourages the concept of analytical model building. It converts the raw facts and figures into information from where valuable knowledge can be extracted out. Here, it takes the temperature data of the last few years and using various algorithm techniques, converts that raw data into valuable information, answering the query of the user if it will rain tomorrow or not? Not only this, but it also helps the data analyzer to depict some additional information like at what time, what place, and how much rain is going to happen.

Evolution of Machine Learning

Evolution of Machine Learning
Evolution of Machine Learning

With the advancement of technology, modern machine learning is not just similar to ancient learning. It works on the principles of pattern recognition and a strong theory of performing specific tasks without being programmed by anyone. The term artificial intelligence was first coined in 1956. At that time, the main aim was to explore topics like effective and efficient problem solving and symbolic methods. Then in 1960 the US Department of defense showed up interest in it and began to train their computers to mimic basic human reasoning. At that time the results were obtained with the help of a concept called neural Networks which excite the logical thinking power of machines.

But, this trend altered in the 1980s till 2010, the machine began to answer the queries using the machine learning algorithms. The technological giants like Google, Microsoft began to enhance their machine learning algorithms in order to get the most accurate results from them. The greatest example is Google’s Driverless Car. Apple’s Siri, Microsoft’s Cortona, etc. Machine Learning (ML) has evolved out as one of the most explored and popular industries. One of the major causes of this boom is the increased data volumes, advanced algorithms, and improvement in computing along with storage capacities.

Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence

You might be wondering that Machine learning and Artificial Intelligence are two different cups of cake. But this is not true. Basically, AI is a broad science that mimics human’s abilities to take action. And on the other hand, Machine Learning is a subset of Artificial Intelligence that analyzes the data provided and predicts various outcomes based on the recurrence of that event in the past. Various tools are used to analyze a large chunk of data and to find the correlation among various attributes so as to predict the desired outcome. Some of them used by Data Analysts are Rapid Miner, Weka, orange, Knime, etc.

Who are using Machine Learning?

Machine learning has extended its hairs everywhere and has not only inspired fresh College graduates but also the researchers to go ahead and do some hands dirty by improving its decision-making power. In today’s era, it is widely used in each and every industry is taking the best decisions.

Some of them are discussed below:

Finance Sector:

The finance sector has a big chunk of data related to its customers. So, it uses the same in determining the chances of return of money if the loan is granted to particular customers having attributes such as age, marital status, account balance, salary, etc. Banks are now hiring data Analysts who determine the probability of a person to be loyal or churn. This has enabled the bank to become extra cautious while lending the money so that there could be fewer chances of getting customers churned.

Health Care:

The health care industry has boomed a lot with the effective and efficient usage of Machine Learning. It has helped in determining the right cause of some diseases like Cancer. Data analysis has not only shown various statistics but has also has enabled health experts to determine the chances of a particular disease being inherited from the parents. This has also helped the Government to frame policies accordingly.

Social Media:

Social media sites like Facebook use Machine learning algorithms in order to increase the user-level experience on their platform. It uses algorithms that not only capture the tastes and interests of the users but also show the related things that the user loves to do. It has helped them to gain a user’s interest and the traffic on their site to a large extent. Now, Facebook is trying to use a similar technique in its advertisement campaigns.

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