After the first industrial revolution, humans adopted a tendency to make the works automatic. By the passage of time, many technologies have been invented by humankind and these are making humans more efficient day by day. This human nature has made computers and now machine learning.
What is machine learning?
Machine learning is not a new concept. The term machine learning was coined in 1959 by Arthur Samuel. This Technology is about making the works automatic. As its name is, it makes the machines efficient to learn things by analyzing the data. It is a subset of Artificial Intelligence. The machine uses the training data in order to make predictions or decisions without being explicitly programmed to do so. Machine learning is closely related to computational statistics. The three basic methods of machine learning are:
Why Machine learning?
Machine learning is being used in every field nowadays due to its attributes. It is making a lot of work automatic which was not an easy task for a human, so this is time-saving. It is exploring new dimensions of computing, as it can be used to make predictions by structured and unstructured data. Today in the medical field, in astronomy, in robotics, in weather forecasting we want to be able to learn by given data. ML is very helpful in these fields. By image processing it can help in finding disease, in astronomy scientists have discovered new planets by processing the images and waves coming from deep space. In the programming field now we have no need to explicitly program for a task because we can make the machines to learn, so a lot of typing can be reduced.
How is it helping us?
Machine learning is making human life easier. Now it is widely used by companies to perform the tasks. In our devices, we have virtual assistants like Siri, Google assistance, Cortana etc.
In image processing, Image and video recognition, video surveillance, social platform, recognizing spam and malware, customer support, search engines and in many more fields ML is being used widely.
Like every technology, Ml has challenges too, but by the time it is being reduced. Today we simply cannot rely on the ML prediction. We need more and more data to train our machine learning model. It has the limitations of ethics, deterministic problems, lack of good data and misapplication, but still, a lot of things can be done in the field of machine learning.