Machine learning in its technical form refers to the scientific study of statistical models and algorithms that are further used by a system or computer. Computers are used to perform an action in an effective manner and that too without any human intervention or explicit change in the program or its build. Machine learning has traditionally been known as one of the subsets of artificial intelligence. During execution, machine learning algorithms create a model of data which is mathematical in nature. This creation of several models is called training data and is often considered while making predictions without having any explicit changes made to the program.

Various models of machine learning
The artificial neural network is one of the main models of machine learning that is used by the industry and its professionals. It is also known as a connectionist system. These are often believed to be influenced by neural networks, which are biological and constitute the brain of animals. The network is not an algorithm on its own, but rather is a framework made up of various machine learning algorithms which then form a virtual framework. These algorithms work collectively to process data which is complex and extensive. It should be noted that such systems function by taking examples into consideration and are not programmed with the rules of any specific sort. The goal of an artificial neural network was originally to find solutions to problems in much the same way a human brain, its structure and inner working, would find solutions. However, over time the focus shifted from its structure and moved into its core capability. This led to its deviation from any resemblance or reliance on biology. Recently it has been detected in use in several tasks ranging from speech recognition, filters in social networking or playing board or video games. Moreover, a few hidden layers of this model often work together to give rise to deep learning. This approach intends to replicate the structure and function of a human mind and can be observed in speech. We may have support vector models which primarily operate on the labelling and categorization of examples. These two models are the most eminent and most widely used among the programs.

The limitations associated with machine learning
Even though machine learning has done wonders in an array of fields, it has been remarked that machine learning sometimes fails to deliver what is expected from it. There can be various reasons connected to this shortcoming. It is known that a lack or shortage of a suitable form of data often leads to undesired results. Not only can there be a shortage of data, but a lack of access can also arise. Another factor causing this shortcoming may be data bias and privacy problems. It has been noted that incorrect selection of tools and lack of proper resources may also lead to results which are far behind expectations.

Software used for initiating machine learning
There are numerous programs serving the purpose of programmers. They may be free and open-source software like CNTK, ELKI or OpenNN and orange. There might also be proprietary software like Amazon machine learning or Google prediction API.

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