The simple terms of supervised and unsupervised learning

The simple terms of supervised and unsupervised learning

The simple terms of supervised and unsupervised learning: Algorithms to solve tasks with machine learning can be broken down into two major types, supervised and unsupervised.

In this article, I want to talk a bit about approaching solving tasks with machine learning.

Generally, I find it helpful to think of supervised and unsupervised learning in the context of a specific example, image classification. In this case, you have been given a bunch of images of vehicle types.


Supervised

Supervised learning means that our training data is made of images and their corresponding class labels. Let’s say you have pictures of cars, bikes, buses, and others, all with their proper name or known as the ground truth. Next, you can train an image classifier that takes in an image as input. The goal is to produce a label that is as close to the actual class label as possible. As you train the classifier, it tries to improve its accuracy.

Supervised learning means we have a particular identified target; in this case, the known label, to aim for during the training process. When the model is highly accurate at learning, we achieve successful training on how to predict actual labels, given new data it hasn’t seen before. In other words, data that wasn’t part of a training set.

Unsupervised

Let’s think about how you might approach this task if we were not given any labels. So our training set is made of unlabeled images. We’ll have to take an unsupervised approach. This means that instead of relying on giving information about how to group all labeled data, we have to find natural groupings in the data.

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