Machine Learning: The Method Of Artificial Intelligence To Make Machines Smarter

06 Sep 2016

In the last few years, the industry of information technology has developed on a wide scale. The new innovative technologies are introduced by the engineers that bring an immense growth in the industry. One of the major aspects of intelligence is the ability to learn, and transforming that power to machines. In fact, the machine learning has become one of the major platforms for developing Artificial Intelligence and create various new opportunities for making machines more intelligent.

Machine Learning: Things About It

Although Machine Learning sounds interesting and beneficial, but it has some limitations. All the machines can’t build intelligent machines due to its limitations. But, there are many real applications where the practical Machine Learning has implemented. The Image Processing, Text Analysis, Data Mining and Video Games & Robotics are some of its examples.

Machine Learning: How Does it Work?

Although, the machine learning concept is new for the society, but people have been working on it for years. The engineers are working to make machines more intelligent. Many of the renowned scientists have written the books on Machine Learning. But, it is still unexplored for many of the peoples. According to the book Machine Learning By Tom Mitchell, it is defined as:

"A computer program is said to learn to perform a task T from experience E, if its performance at task T, as measured by a performance metric P, improves with experience E over time."

For Example: Provide a machine some personal information about a person and assigns a score mentioning the chance of paying a credit loan by that person. In this process, the task is to assign a credit score, and the user profiles are set with corresponding credit scores. Here, the performance will be measured on the basis of the difference between the predicted and expected score.

According to Mitchell’s definition experience E point, "Through Training Instances, we can allow the algorithm to learn to transform the input to the desired output." Moreover, while applying the Machine Learning concept, the calculating and selecting the proper features is one of the essential tasks performed to represent an instance.

Categories of Machine Learning algorithms

There are two types of Machine Learning algorithms i.e., Supervised Learning and Unsupervised Learning algorithms. The major difference between both algorithms is the method used for providing training examples to the algorithm, how the algorithm uses them, and the type of problems they solve.

Supervised Learning

Supervised learning is one of the most significant categories of Machine Learning algorithms. In this, the Machine Learning algorithm is considered as a process that is used to transform a particular input to the desired output. For this, the machine has to learn how to transform each possible input to the desired output. To get the proper results, the machine is provided with the training module which has particular input and the desired output.

Unsupervised Learning

The second important category of Machine Learning algorithms is Unsupervised learning. In this process, the training examples are required to input to the algorithm. There is not need to add the desired output. Through this process, it is easy to find out the hidden structure and relations between the training examples.

Conclusion:

The Machine Learning is powerful, but it has some limitations. Through Machine Learning, people can obtain decent results, but can't solve all the difficulties with IT and software development services. Moreover, for good performing applications, it is necessary to build a robust and performant solution, which is really difficult with the Machine Learning, as you have to select the Machine Learning framework which is compatible with the programming language. Still, there is a long way to go with Machine learning.

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