Machine Learning Guide for Everyone: Introduction

In reality, we have to work with the datasets which have a high number of features, in other words, high dimensionality. So this increases the computation time and decreases the performance of the model. So to deal with the issue we use Dimensionality Reduction. It works by finding correlations between features and removing redundant information and then assembling specific features into high-level ones. It also helps in removing the noise from the data. It is used in- Recommender Systems, Fake image analysis, etc.