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Essentially, this technique focuses on the development of computer programs that can access data and then use it learn themselves. It is actually a classic method of learning that learns how to learn. All one needs to know is the basic mechanism so that one can use it well. After all, there is always a difference between making use of something and making efficient use of something. This efficiency can come only we understand it completely and know all the aspects that it, in this case, machine learning operates on.
Machine learning uses data analysis that automates analytical model building. The basic premise is that systems can learn from data, identify patterns and make decisions with minimal human intervention.Naturally, with less human intervention, there would be more growth as there would be minimal dependency of human beings. All one does need is a few human beings who program it intelligently and efficiently to not just do it once but many times and also keep learning from each experience of the machine itself. This may sound like science fiction but the fact is, this is the new reality.
Evolution of the new reality
Due to new computing technologies, machine learning today is not like machine learning of the past. When it began, it was based pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Soon, researchers interested in artificial intelligence wanted to see if computers could learn from data. Here, the repetitive aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. Machines learn from previous computations. By analysing this, they produce reliable, repeatable decisions and results. This is not something that is new but in recent years, it has gained fresh momentum.
Why is this important?
More interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Primary factors are growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
Taken together as all of this is happening at the same time, it means that it is now possible to quickly and automatically produce models that can analyze huge amount of data and deliver faster, more accurate results. Therefore, organisations are making use it. They do this by building precise models so that they have a better chance of identifying profitable opportunities – or avoiding unknown risks.
The fact is many machine learning algorithms have indeed been around for a long time. However, the ability to automatically apply complex mathematical calculations to huge amounts of data that is known as big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications we may be familiar with. In fact, the beauty of machine learning is that many of us are using it without even knowing this is what we are using.
- The heavily hyped, self-driving Google car? This is the essence of machine learning.
- Recommendation that are given online to offers such as those from Amazon and Netflix? This is Machine learning applications for everyday life.
- Customer views about you on Twitter? This is machine learning combined with linguistic rule creation.
- Fraud detection? This is one of the very important uses of machine learning in our world today.