Are you one of those people who think Machine Learning and Artificial Intelligence are the same things? Well, rest easy, you’re not alone.
The three terms, namely ML (Machine Learning), AI (Artificial Intelligence), and DL (Deep Learning) are closely related and for someone who’s not an expert in the field, can seem almost the same thing. In this article, we break down the differences between the three, so read on.
What is Machine Learning?
As the name immediately suggests, Machine learning can be associated with somehow giving machines the ability to ‘learn’ new things themselves.
They are basically algorithms (or programs) that alter themselves as they explore the world around themselves and learn new things. ML, therefore, is more of a subset to AI — a technique of actually implementing it.
The ‘learning’ part in ML comes where programs or algorithms attempt to optimize themselves at one particular thing or along a particular dimension. They try to minimize the error possibility and make their predictions more likely to be true.
This is called an objective function in data science jargon. The core intention of ML is to allow algorithms and programs to learn by themselves in accordance with the data they are exposed to and make accurate predictions.
In a nutshell, ML algorithms can be understood as optimization algorithms. If tuned correctly and fed the right data, they can minimize their error probability by guessing the solution over and over again.
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Difference between ML, AI and DL
As aforementioned, ML is a subset of AI. This means that all ML counts as AI, but AI doesn’t count as ML. ML is one of the numerous ways we have to implement AI in the real world.
ML is simply a method of training programs so that they can make their own decisions. DL (Deep learning) on the other hand is a subset of ML. When people talk about DL they are actually referring to deep artificial neural networks.
Deep artificial neural networks here refer to sets of algorithms that can reduce error to achieve extraordinary accuracy for many important implementations. These implementations consist of image processing and recognition, sound recognition, recommendation systems and so on.
‘Deep’ is a technical term here. It refers to layers in the neural network. The deeper the network goes, the more layers get added and hence more accuracy.
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