Understanding Machine Learning

 

Understanding Machine Learning

Understanding Machine Learning

We have heard the term ‘machine learning’ in different discussions and forums, but what does it exactly mean? Machine learning can be defined as a method for data analysis, based on pattern recognition and computational learning. It comprises of different algorithms like neural networks, decision trees, Bayesian networks etc. Machine learning uses these algorithms to learn from data and recover hidden insights from data. The learning process is iterative, so the new data is also handled without any supervision. The science (machine learning) to learn from previous data and use it for future data is not new, but it is gaining more popularity nowadays.

What is machine learning?

While some people believe that machine learning is no better than the old method of computer programming that we still use now, many consider it as a revolution the field of artificial intelligence. They believe that using this technology, machines will be able to learn things and do things with their own experience, rather than dumbly follow human instructions.








To understand more about the meaning of machine learning, we can compare it to normal computer programming. The following sections will discuss more about the machine learning and its difference from traditional programming.

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What is traditional programming?

When we program a computer, we actually give directions to it in a language that it understands. Then, when we give it an input, it gives a reasonable output based on the instructions that we have given to it.

Now, let’s imagine that you have given an input to apply for a credit card. While processing your input, the system will look at all the important parts of your application, take the necessary information and process it. After that, it’ll produce the output of acceptance or rejection based on the program that was fed to it.

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How machine learning is different?

If you use machine learning in the place of traditional programming methods in the credit card scenario, then the result would have been somewhat different. The result will actually be based on the input data and the system will gain experience by processing that input data. There won’t be any special program for it. As it’ll gain more and more experience, its performance will get better with time.

So, actually machine learning is a part of AI or Artificial Intelligence. It learns by analyzing the large quantity of data files made with each usage of the system. As it’ll analyse the data, it’ll change its programming according to newer demands. This leads to improvement in its accuracy too. We can also say that machine learning is like a linear regression, where the variables and parameters are changed to better match the input provided.

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What are the popular machine learning methods?

The most popular methods of machine learning are unsupervised and supervised learning methods. Among these, the supervised method is most commonly used. About 70% is supervised and 10-20 percent is unsupervised. Semi-supervised and reinforcement learning is also used in many cases.

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Supervised learning

In this method, the algorithms are included with labelled examples, where labelled data means that the data is given a description. The machine learning system will receive both some inputs and their corresponding outputs. Now, the system can get more experience by comparing the outputs with the correct outputs to find the errors.

After analyzing the outputs and finding out the errors, the system will change its programming accordingly. The system can use different methods to predict the label on unlabeled data. This method is used to do future event predictions based on past data.

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Semi-supervised learning

This kind of machine learning method is used in similar places, but it also uses unlabeled data while training. Usually, it works with unlabeled data more than labelled ones, but it uses the labelled ones too. This is because unlabelled data can be gathered easily. Here, unlabelled data refers to those data which lack any description at all.

This method also has the same factors of learning, i.e. prediction, classification and regression. This is the best when the cost involved with supervised learning is too high.

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Unsupervised learning

This method is used for those data which doesn’t have any labels at all, i.e. unlabelled data. Thus, the system knows nothing of the correct output and so, the algorithm has to figure out the correct output itself. It can do this by finding some structures within the data. This type of machine learning method is perfect for transactional data. The factors of learning here are nearest-neighbour and self-organizing mapping, along with singular value decomposition and k-means clustering.

Reinforcement learning

This machine learning method is used in places like gaming, vehicle navigation and robotics. In this method, the system learns by a trial and error method. The main goal in this method is to find out the output in the least time, which can be done by following a suitable policy. The three components in this learning method are the agent which learns the environment to interact with and the actions to do.

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What is the importance of machine learning?

The importance of machine learning is growing day by day due to the same reasons that have made the concept of data mining so important. These reasons include cheap and powerful computational power, large amounts of data being created every day and inexpensive and high-capacity data storage methods. These factors ensure that powerful and accurate models can be made very quickly, which can be used in analysis big data reserves in bulks, in order to get better, highly accurate outputs. This means that better models can be used for making better corporate and business decisions at lesser time, and that too without any human work.

One way to make such accurate models quickly is automated model making. Also, this model should be dynamic enough to keep up with the changing times. While humans can create two models a week, machine learning can create thousands of such accurate models by analyzing data quickly. So, that is why machine learning is so important for businesses and other fields nowadays.

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What are the machine learning algorithms and processes?

Machine learning algorithms help in getting the most out of big data, by helping the system to quickly analyse the data and produce accurate results. These algorithms help in creating a model which can be used for making business decisions. Some algorithm types are neural networks, random forests, k-means clustering, self-organizing maps and nearest-neighbour mapping.

However, using just algorithms is not all. The best model can be only made by following a suitable process. Some examples of such processes are comprehensive data management, interactive exploration of data and visualization of the results found by the model.

What is the impact of machine learning on business?

The impact of machine learning on business is tremendous. Machine learning has opened new possibilities for businesses. It has helped in accurate model making which have in turn helped in making better and smarter decisions quickly.

Some uses of machine learning have been mentioned in the next section.

Some practical use cases

Machine learning is being used in many places these days. Let’s have a look at some of them and what they actually do.

Promedas, medical software or a “desktop doctor”, is a machine learning based program uses hundreds of years of medical knowledge to help the doctors in detecting the disease.

Also, Amazon has automated its employee access granting and revocation through a computer algorithm which can predict the resource access for each employee.

Machine learning algorithms are also being used by Cornell University to detect whales in the ocean through sound recordings, so that ships can avoid them.

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Conclusion

As data is growing day by day, the importance of effective processing is also growing. For this, machine learning methods have been devised, which helps the system in making smart decisions, almost without the involvement of humans. The present impact of this technology on the IT sector has been tremendous, so it is easy to imagine that it’ll still be very important in the future too.

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