Unsupervised learning-Clustering techniques

This article Introduces concepts of unsupervised learning with a focus on implemeting some Clustering techniques using Scikit-Learn

Unsupervised learning :

Unsupervised learning is often the case in the real world, that data is unlabeled. You might apply an unsupervised learning technique to make unlabeled data self sufficient. For example, if you want to identify photos of a specific individual, you might feed a model lots of different photographs, millions of them until it starts identifying similar features. Unsupervised learning techniques are also used for latent factor analysis, anomaly detection, quantization, especially with colors. or as pre-training for supervised learning problems, such as classification and regression. Of all of the unsupervised learning ML techniques, there are two that are very popular and widely used, autoencoders and clustering.

Supervised Vs Unsupervised Learning

machine-learning algorithms fall into two broad categories, supervised and unsupervised learning.

• Supervised learning algorithms seek to learn the function ‘F’ that links the input features with the output labels. So you can think of supervised…