Learning can be explained as a process which improves performance from experience, an extension to this definition would be, the process of Machine Learning (ML) which can be explained or defined, as a method through which computer programs, that habitually or spontaneously improve their performance through experience.
This would basically translate into machine’s learning to improve their performance based on limited programming interventions. ML can be considered as an extension to Artificial Intelligence, which believes that Machines should be able to adapt and learn through experience.
ML is not a new innovation and has been around for years, however with new computing technologies, ML has evolved, most of the algorithms have been around, however, the ability to apply complex algorithms to big data, in a loop and more rapidly, is a recent development.
ML is quite integrated in our everyday lives, so much that we might not be consciously aware how frequently we are using the application. For example, there is great excitement over Google’s Self Drive Car, a product of ML. Spam emails being diligently dumped away, or frequent recommendations while shopping online, or offers from particular brands of your interest being brought to your notice, are all direct outcomes of Machine Learning.
Besides the basic applications, more recent complex uses of ML would be early fraud detection in banking, a lot of businesses are able to have a consolidated look at what their customers feel about them, emotional and sentimental analysis is possible through data mining techniques, again a direct product of Machine Learning.
In current times Machine Learning matters, for the possibilities and advantages it offers. There are growing volumes of data available to us easily, computational processing is cost effective, and we have better data storage opportunities, all this indicates that we are right in the center of exciting times, where, we will be able to analyse bigger and complex data faster and more accurately.
Which directly means that organisations will have a better vantage point to make informed decisions, leading to better profits and avoiding risks.
The Advantages of Machine Learning
With a good investment of time in creating training data for machines, learning can then be expedited through experience and learning through algorithms. Implementation and automation then become easy for machines, upon learning, a machine can process several images without any fatigue as opposed to a human brain, which might deliver data with errors.
With good training data input and intelligent processing, with an accurate algorithm, the output can be phenomenal. Hence it is believed that big data and Machine Learning is a great combination, opening doors to various opportunities.
Application of Algorithms in building models may expose links which can help an entity make better decisions with minimal human interventions, keeping biases away.
Most organisations in recent times have understood the importance, benefits and value of Machine Learning technology, as most insights from the available data can be received in real time, hence giving companies an edge over their competitors, and assisting them in better aligning their needs with those of their customers.
Due to these paybacks, application of Machine Learning can be seen in Financial services, Healthcare, Marketing and Sales, Transportation and logistics, Government agencies like Utilities and Public Safety.
So while machine learning has many advantages, it has a few challenges, however, the benefits of the application outweigh the limitations. The ability to decipher big data, with minimal programming, faster and accurate results in real time, will see Machine Learning be applied in various aspects of our daily lives.
