Supervised learning and unsupervised learning
This article explains about basic Machine Learning concepts of Supervised learning and Unsupervised learnings.
Let’s say, you want to develop Machine Learning algorithm to detect whether car exists in a image
Supervised learning
In supervised learning you'll label several images of cars and non-cars and an algorithm will eventually learn and be able to predict whether or not an unseen image has a car in it. For supervised learning the feeded data must be labeled, or explicitly mention which ones are cars and which ones aren't.
When you complete a Captcha and identify the images that have cars, you’re labeling images! A supervised machine learning algorithm can now use those pictures that you’ve tagged to make it’s car-image predictor more accurate.
Mathematical definition
In the supervised learning paradigm, the goal is to infer a function f : X → Y , the classifier, from a sample data or training set An composed of pairs of (input, output) points, xi belonging to some feature set X , and yi ∈ Y :
An = ((x1,y1),...,(xn,yn)) ∈ (X ×Y )n.
Typically X ⊂ IRd , and yi ∈ IR for regression problems, and yi is discrete for classification problems. We will often use examples with yi ∈ {−1, +1} for binary classification.
Unsupervised learning
References:
https://link.springer.com/chapter/10.1007/978-3-540-75171-7_2
https://www.researchgate.net/publication/342121950_What_is_Unsupervised_Learning/link/5ee30fbe299bf1faac4e78f1/download
https://www.codecademy.com/article/machine-learning-supervised-vs-unsupervised
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