FEBRUARY 20208MACHINE LEARNING-CONNECTING PRESENT THROUGH FUTUREBy Raja Saravanan, Director, EllucianAs an industry expert, Raja is proficient in strategic planning and leading development & delivery of CRM solutions based on business prioritiesIN MY OPINIONachine learning has be-come a buzz word today with most software prod-ucts or services claim-ingthat they are powered by ma-chine learning. In the hype cycle, machine learning is reaching the peak of inflated expectations. As a result of this, there is a widespread belief that machine learning is a silver bullet to all problems. While some of it may not be realistic, at least with current technology, it does offer very interesting possibil-ities that we could not have imag-ined a decade ago.Demystifying Machine LearningMachine learning is not a new tech-nology breakthrough, it has been here for many decades. It is getting more attention now as it has become commercially viable to implement applications with machine learning abilities, thanks to advancements in computing technology. If you are new to machine learning, you should understand the fundamen-tal paradigm shift in how tradition-al programs were written and how a machine learning-based program is written.Traditionally programs were given a set of inputs. Rules are pre-coded into them which when applied over the inputs gives the result. A machine learning program takes in a set of inputs and the cor-responding outputs, it then figures out the rules of the program that when applied to the input gives the corresponding output.In the example illustrated in the figure, the machine learning pro-gram makes an educated guess of the rules when applied to the inputs to deliver a certain output. In real-ity, the rule is not as simple as that of a sum, it could be a linear regres-sion model. One advantage ofletting a program predict the rules is that the program can adapt itself to the changing behaviors of inputs and outputs to appropriately determine the new rules. How close the out-puts are when applied to the inputs determines the accuracy of the ma-chine learning program. The pro-gram when determining the rules checks the accuracy of the outcome which is known as feedback. Based on the feedback it can refine the rules until the desired accuracy is achieved. This iterative process of determining the rules is called ma-chine learning.Accuracy is usually denoted as a percentage. Once the program achieves the desired accuracy it stops learning. It now has a mod-el that can predict the output for a new set of inputs. The words accu-racy and predict are used, as there is always room for error. This is what makes a machine learning program more human-like than being just binary true or false. Unlike a tradi-tional program, a machine learning program is tolerant of errors that open up new possibilities to be ex-plored. In a movie streaming service like Netflix, movie recommendation MRaja Saravanan, Director
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