Unsupervised learning is a fairly complicated learning model compared to supervised learning, in which the data that we are providing to the model to learn from is not labeled. It means that by reading the data, it cannot tell whether it belongs to class A or B, or if it's related to male or female, and so on. In these types of scenario, where the data is not labeled, we apply different algorithms so that a machine can at least distinguish between different sets/classes/categories within the provided data.
To understand it more clearly, let's take an example dataset to distinguish between different fruits based on their weights. By looking at the following chart, one can easily tell that if the weight is between 140 to 150 grams, it's a mango. Similarly, if the weight is between 110 to 120 grams, it's an apple, and if the weight is between...
