Introduction to unsupervised learning algorithms

Introduction to the basics of machine learning, in this article you will be able to learn different unsupervised learning algorithms in machine learning and how they work…

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Unsupervised Learning Algorithms

Unsupervised learning algorithms apply the following techniques to describe the data:

• Clustering: it is an exploration of data used to segment it into meaningful groups (i.e., clusters) based on their internal patterns without prior knowledge of group credentials. The credentials are defined by similarity of individual data objects and also aspects of its dissimilarity from the rest (which can also be used to detect anomalies).
• Dimensionality reduction: there is a lot of noise in the incoming data. Machine learning algorithms use dimensionality reduction to remove this noise while distilling the relevant information.

You would eventually come across two other types of machine learning That are getting more and more attention, read carefully.

• K-Means Clustering is an unsupervised machine learning algorithm. In contrast to traditional supervised machine learning algorithms, K-Means attempts to classify data without having first been trained with labeled data. Once the algorithm has been run and the groups are…