Introduction of Unsupervised Machine Learning.

Unsupervised learning could be a machine learning technique in which models are not supervised using a training dataset. Instead, models themselves find the hidden patterns and insights from the given data. It is compared to learning which takes place in the human brain while learning new things. Machine Learning Course gives you an introduction to machine learning along with the wide range of machine learning techniques like Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

It Can be defined as Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and are allowed to act on its data without any supervision. Unsupervised learning cannot be directly applied to a regression or classification problem because, unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of the dataset, group that data according to similarities, and represent that dataset in a compressed format.

Example: Suppose the unsupervised learning algorithm is given an input dataset containing images of different types of cats and dogs. The algorithm isn't trained upon the given dataset, which means it does not have any idea about the features of the dataset. The task of the unsupervised learning algorithm is to identify the image features on their own. An unsupervised learning algorithm will perform this task by clustering the image dataset into groups according to similarities between images.

Why use Unsupervised Learning?

Below are some main reasons which describe the importance of Unsupervised Learning:

Unsupervised learning is helpful for finding useful insights from the data.

Unsupervised learning is much similar to human learning to think by their own experiences, which makes it closer to the real AI.

Unsupervised learning works on unlabeled and uncategorized data which makes unsupervised learning more important.

In the real world, we don't always have input data with the corresponding output so to solve such cases, we need unsupervised learning.

Working on Unsupervised Learning

Here, we have taken unlabeled input data, which means it's not categorized, and corresponding outputs are also not given. Now, this unlabeled input data is fed to the machine learning model to coach it. Firstly, it'll interpret the raw data to find the hidden patterns from the data and then will apply suitable algorithms like k-means clustering, Decision tree, etc

Once it applies the suitable algorithm, the algorithm divides the data objects into groups according to the similarities and differences between the objects.

A detailed video is available on the Youtube link: https://youtu.be/8jpwLVJiYmM

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