Myths About Machine Learning

By NIIT Editorial

Published on 11/12/2021

If you haven’t read or even remotely heard of machine learning as a revolutionary technology then you need to dig your GK out of the inner depths of mother earth. ML is proving a wondrous invention emerging from data science that powers the undercurrent of data-first companies. Uber, Google, Facebook, and Netflix are just a couple of Big Tech corporations that are monetizing the machinations, pun intended, of ML at pace. But in the midst of such breakthroughs, there are times when information gets over-exaggerated. With ML that is too often the case and it is to dispel such myths associated with machine learning, that we will be focusing on today.  

Myth #1 - Machine Learning is the same as AI and Deep Learning 

Although the terms may be used interchangeably because of a correlation, however, but the ground reality is different. Machine learning has helped usher the age of Artificial Intelligence and therefore it can be considered a legitimate sub-sector of the same. Similarly, deep learning has emerged from machine learning and can be considered a sub-sect of ML. In essence, this triad can be clubbed together under the umbrella of data science. 

Myth #2 - Machine Learning will cause mass job displacement 

Machine learning is being used to automate digital workloads and mechanize repetitive human-dependable tasks. Therefore, only a small spectrum of lower-rung job roles are bound to be affected by its onset. But in the broader context of things, such technologies will only aid humans in business operations. An option could be to upskill in machine learning and data science to improve your prospects of getting a job. If interested do explore NIIT’s Advanced PGP in Data Science & Machine Learning

Myth #3 - Machine Learning can tell the future 

There is a half-truth to this statement. Of course, machine learning is a science that has been devised to predict future possibilities and then selects the best course of action. However, ML can only deduce permutations of situations. It is no crystal ball with the power to say authoritatively what is going to happen for sure. Not to mention, for ML predictions to come even close to reality, the data has to be equally useful. 

Myth #4 - One needs to be Ph.D. in Maths to learn Machine Learning

This is partially true. The applications of mathematics are more towards research-oriented projects that inadvertently push the boundaries of deep learning. However, for professionals who are more interested in training ML data sets and deploying the same on production, then MLOps would be a better option to pursue. In addition to this, expertise in model architecture, and matrix functions is also expected. 

Myth #5 - ML findings are to be taken as it is  

Data is a challenging asset to monetize. To perform accurate predictive analytics, data scientists must have not just cleanly engineered data sets but also relevant ones. Not to mention, there could be inherent data bias in the set being operated on which inherently adulterates the output. 

Myth #6 - Machine Learning Algorithms only discover correlations between pairs of events 

This type of generalization doesn’t fully stand its ground. Machine learning has branches categorized into supervised and unsupervised learning. Depending upon the type of technique applied, algorithms can help uncover findings that weren’t even part of the project scope and this could easily go beyond just discovering correlations between pairs of events. 

There you go. These were some of the most common myths associated with the field of machine learning. If you’re interested in pursuing machine learning as a career, explore NIIT’s premier offerings and carve a future-ready career. 

Myth #7 - ML can solve all Issues 

It couldn’t be any further from the truth. Supervised and unsupervised learning are still in the nascent stages of development. It would be some time before algorithms can be tasked with world-level problems, of an existential magnitude.

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