In this section you will learn about the different types of dimensionality reduction techniques, why do we need such techniques, how to implement and apply them. We will go deeply over Principal Component Analysis (PCA) and how to use it to reduce a given dataset’s dimensions. 

In this first subsection, we introduce the topic of dimensionality reduction, feature selection and feature extraction. After studying this subsection, you should be able to:

• Identify the meaning of dimensionality reduction
• Identify the need for dimensionality reduction
• Differentiate between feature selection & feature extraction dimensionality reduction techniques