In recent years the term “machine learning” has been popping up in various discussions and forums, but what exactly does it mean? Machine learning can be defined as a method for data analysis, based on pattern recognition and computational learning. It is comprised 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 to learn from previous data and use it for future data is not new, but it is gaining more popularity.

What Is Machine Learning?

While some people believe that machine learning is no better than traditional methods of computer programming that are still in use, many consider machine learning to be a revolution in the field of artificial intelligence (AI). They believe that using this technology, machines will be able to learn things and do things with their own experience, rather than to simply follow human instructions.

To understand more about the meaning of machine learning, we can compare it to traditional computer programming. The following sections will discuss more about the machine learning and its difference from traditional programming. (For some of the pros and cons of machine learning, see The Promises and Pitfalls of Machine Learning.)

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What Is Traditional Programming?

When we program a computer, what we are actually doing is giving it directions in a language that it understands. Then, when we give it an input, it gives an 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 will produce the output of acceptance or rejection based on the program that was fed to it.

Read Also: What is Artificial Intelligence(AI)?

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 be somewhat different. The result would 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 gains more and more experience, its performance will get better with time.

So, machine learning actually learns by analyzing the large quantity of data files made with each usage of the system. As it analyzes the data, it changes its programming according to newer demands. This leads to improvement in its accuracy as well. 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.

After analyzing the outputs and determining 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.

The Most Popular Machine Learning Methods

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

Supervised Learning

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

Semi-Supervised Learning

This kind of machine learning method is used in similar instances, but it also uses unlabeled data while training. Unlabeled data is anything that can be obtained naturally from the world but does not possess any sort of explanation or description. Usually semi-supervised learning works with unlabeled data more than labeled data, but it can use labeled data too. This is because unlabeled data can be gathered easily.

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