In the previous lessons, we dealt with supervised learning algorithms to perform classification and regression. We used training data to train our classification or regression model, and then we validated our model using testing data.
In this lesson, we will perform unsupervised learning by using clustering algorithms.
We may use clustering to analyze data to find certain patterns and create groups. Apart from that, clustering can be used for many purposes:
Market segmentation detects the best stocks in the market you should be focusing on fundamentally. We can detect trends, segment customers, or recommend certain products to certain customer types using clustering.
In computer vision, image segmentation is performed using clustering, where we find different objects in an image that a computer processes.
Clustering can be combined with classification, where clustering may generate a compact representation of multiple features, which can then be fed to a classifier...