Hi Community!
When we talk about machine learning, the term should be self-explanatory, but many people are confused about what we're talking about, so to offer you a very thorough yet concise response to what is machine learning? It's essentially a machine, robot, computer, or even a speaker that learns over time without being explicitly programmed, gathers data, and learns from it without the assistance of humans.
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It has two learning modes: supervised and unsupervised.
The supervised learning is done in the context of classification, such as when we wish to create a regression, an artificial neural network, or a naïve bayes model. During the training phase, the system is fed a large amount of data that instructs it on what output to expect from each specific input value. The trained model is subsequently supplied with test data in order to verify the training's outcome and assess its accuracy. When we have a lot of data, the outcomes are much better; the process can be frustrating at first, but the end result will be as planned.
Unsupervised learning is the use of artificial intelligence (AI) algorithms to find patterns in data, such as when you have molecules, part of which are medications and part of which are not, but you don't know which are which and want the algorithm to figure out which are which. Alternatively, suppose you have a collection of images of six people but no information on who is in which one, and you wish to divide the dataset into six piles, each containing the photos of a single person.
Machine learning is a large field with a lot of knowledge, it has a lot of real-world applications, and we know it's the future, along with programming, artificial intelligence, and deep learning with data science. I hope you like this article and that you will share it with your friends and follow us on social media!
