AI vs Machine Learning: Difference Between AI and ML – Have you ever wondered what the difference between AI and machine learning is exactly? Well, if you have, you’re not alone. In this article, I’ll be discussing those differences so that you’re good to go moving forward. In the last article, we talked about what AI is and what AI isn’t. And we said that AI is essentially intelligence exhibited by machines.
Meaning the learning, understanding, and using the knowledge learned to carry out one or more tasks or goals. So something like a prediction or recommendation. In this case, the learning part is machine learning and you can consider it to be a subset of AI. You may have come across a lot of different definitions of machine learning. We’ll talk about some of my preferred definitions now.
So in this case, a non-technical definition that I like to give for machine learning is that machine learning is the ability to automatically learn from data without requiring any explicit programming or any explicit domain expertise. In fact, that’s what gives machine learning its magic or its secret sauce. The learning is performed by different algorithms that fall under the machine learning umbrella.
Algorithms you may have heard of ,like decision trees or neural networks or regression. Now another more technical definition of machine learning that you may have come across is that machine learning is all about learning a target or mapping function that maps input variables to output variables kind of like an equation does.
Now, in this case, we’re going to talk more about the non technical definition of machine learning. But, of course, put some comments below of any other definitions of machine learning that you’ve come across or ways you think of it. Now, I’m oversimplifying here. But the idea behind machine learning is basically you could take a machine learning algorithm, hand it some data that’s specific to a specific domain, say healthcare.
Then that machine learning algorithm can automatically learn without any explicit programming, all the underlying relationships, patterns, correlations, and so on that exists within the data so that it can do something like let’s say, make a prediction. In machine learning, the idea of learning from experience, which you may have also heard of, it’s just the idea of continuously learning and improving your machine learning models over time with new data.
You know, things change over time like due to regulations or the environment or different trends. And so the underlying data change tends to change with it. Without this sort of feedback loop that people tend to create to constantly take new data and retrain models and make sure they stay fresh, you can wind up with what’s called model drift or stale models.
So, again, AI is simply intelligence exhibited by machines. Machine learning is the process by which machines learn from data in order to be able to do things like make predictions or recommendations
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