“Sir, do you have any project related to machine learning?”
A second-year undergraduate student asked me this question, eyes gleaming with curiosity and ambition.
I felt genuinely pleased—it’s always encouraging to see students eager to explore new areas. But alongside that, I was intrigued. Why the specific interest in machine learning?
“Is there a particular reason you’re looking for a machine learning project?” I asked.
“If I learn machine learning, I’ll get better career opportunities,” came the candid reply.
I paused to reflect. It’s a valid reason. After all, college education is often seen as a path to employability. In today’s job market, having a competitive edge is crucial—and machine learning has rapidly emerged as one of the most sought-after skills by companies offering attractive roles.
But this exchange made me think deeper—not just about skills, but about the bigger picture.
In the 19th century, when automobiles were first invented, every component was handcrafted and assembled manually. The process was slow but labor-intensive, offering jobs to skilled workers who knew how to build and fit parts together.
Then came Henry Ford and his revolutionary moving assembly line—a system that changed manufacturing forever. While some jobs were lost, new ones were created to design, operate, and optimize the new process. The focus shifted from craftsmanship to process efficiency.
Later, the arrival of computers triggered another transformation. Computer-Aided Manufacturing (CAM) streamlined production planning and execution. Jobs based on outdated manual skills gave way to roles that required understanding systems and automation. At every step, companies aimed to reduce costs and boost efficiency—constantly evolving to stay ahead.
Now, Artificial Intelligence—and especially Machine Learning—is doing the same.
It’s no surprise that companies are replacing employees with obsolete skills in favor of those proficient in modern tools and techniques. Disruption, though unsettling, is part of progress.
“Sir, I’ve taken an online course on Machine Learning,” the student added, bringing me back from my thoughts.
That’s good. Machine learning is undoubtedly a powerful skill. But here’s the question I often find myself asking:
Is learning the tool enough, or should we also understand the process it’s meant to improve?
Think back to those major technological leaps. Ford didn’t just introduce a tool—he reimagined the assembly process. John Parsons and Patrick Hanratty didn’t just use computers—they pioneered numerical control and CAD/CAM systems that reshaped manufacturing. Arthur Samuel, in 1959, created a checkers-playing program that laid the foundation for machine learning. And even earlier, in the 1950s, Alan Turing asked the bold question: Can machines think?
Machine learning, as a concept, has been around for over seven decades. So why did it take so long for industries to embrace it?
Because tools are only as useful as the understanding of where—and how—to apply them. It’s not just about knowing machine learning. It’s about knowing what problem to solve with it.
I smiled at the student and said, “Yes, I do have some problems you could work on. They may not look like typical machine learning projects at first glance, but they will help you deeply understand the underlying process. And once you do, you’ll see how machine learning can be used meaningfully to improve it.”