Dimensionality Reduction

The Pathway is about different ways of transforming data from higher dimensions to lower dimensions for several reasons in an optimized way.

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality Reduction refers to techniques that reduce the number of input variables in a dataset.

More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality. These techniques are generally used for Data Visualization but they can also be applied in Machine Learning to simplify the dataset in order to better fit a predictive model.

In this pathway, we are going to learn:

• What is Dimensionality Reduction?
• Different Algorithms and the intuition behind it