Mathematics for Machine Learning with Python- CST284 KTU Minor - Dr. Binu V P -9847390760

An important machine learning method for dimensionality reduction is called Principal Component Analysis. It is a method that uses simple matrix operations from linear algebra and statistics to calculate a projection of the original data into the same number or fewer dimensions. In this tutorial, you will discover the Principal Component Analysis machine learning method for dimensionality reduction and how to implement it from scratch in Python. Principal Component Analysis, or PCA for short, is a method for reducing the dimensionality of data. It can be thought of as a projection method where data with m-columns (features) is projected into a subspace with m or fewer columns, whilst retaining the essence of the original data. The PCA method can be described and implemented using the tools of linear algebra PCA is an operation applied to a dataset, represented by an $nxm$ matrix $A$ that results in a projection of $A$ which we will call $B$.  Let's walk through the steps o