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Basic concepts of Machine Learning

This article is about Machine Learning, we will be focusing about the basic concept behind machine learning and some terminologies associated with it. We will discuss about its basic mechanism, Artificial Intelligence, analogy between AI and Internet of things (IOT).
Submitted by Sudarshan Paul, on June 15, 2018

Understanding Machine learning

Machine learning is a subset of artificial intelligence which deals with machine’s ability to learn and work on improvisation from the past experiences that they are exposed to, without the need to explicitly program them through human intervention. The technology aims to make machines smarter and efficient.

Machine Learning has been classified into three categories based on the method of training the machines during the process.

1) Supervised Learning

In this method of training, the computer is fed with both the input and the output. The feedback during the training is also provided to the computer. Based on that the accuracy with which the computer predicts during the training is analyzed by experts. Supervised learning thus enables the machines to associate the input with the appropriate output.

2) Unsupervised Learning

In this method there is no supervision or tracking of the input or output, the computers are allowed to interact and produce the output on their own without any sort of intervention. It is mostly applied for transactional data and to perform more complex tasks. Further explanation of this topic is beyond the scope of this article, which will be discussed in another topic called Deep Learning.

3) Reinforced Learning

In this method of learning, there are mainly three components which work together – Agent, Environment, and Action. The agent is responsible for identifying and perceiving the surroundings, the agent interacts with the component named environment, which in turn produces the necessary action.

Working mechanism of machine learning

If you have heard of the term data mining, the machine learning utilizes the similar processes for its operation. The Machine learning algorithms can be defined in terms of a target function, let's name it f() that contains the input variable (x) and a respective output variable (y). Thus the above relation can be represented as: y= f(x)

So as to obtain the closest results we use an additional correction factor in the above expression e, which is not dependent on the input variable x. Therefore we obtain the equation: y= f(x) + e

In machine learning, we commonly use the term mapping from x (input variable) to y (output variable) to make predictions. This method of mapping x and y to get accurate predictions is referred to as Predictive Modelling. A list of useful assumptions can be made based on this function.

Difference between Machine learning and Artificial Intelligence

Artificial Intelligence and Machine Learning are often considered very much related and people seem to use them interchangeably in discussions. They are not exactly synonym of each other but rather one is the outcome and the other result of it. In concise Artificial Intelligence refers to the ability of machines to perform tasks that we humans usually perform, by imitating the human thinking and perception. On the other hand, we can consider Machine Learning as one of its process or application by which we only provide the machines with appropriate data to work upon and let them learn themselves from experiences.

Relation between Artificial Intelligence and Internet of Things (IOT)

The Artificial Intelligence and IOT share a relationship of that of our body and brain. For example, our various body parts collect sensory inputs from surroundings such as reflected light. The work of brain is to convert the received light input in the form of reflected light into recognizable objects. If we compare our body parts to the IoT devices and our senses to the various sensory equipment attached to the devices which interact with the surrounding and collect inputs. The Artificial Intelligence acts like our brain which understands the inputs and takes part in the decision of what response is to be given next with respect to the inputs received. Thus, they are very much interrelated but obviously not the same thing.






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