Principal Component Analysis is one of the bread and butter dimensionality reduction methods for unsupervised learning. One of the assumptions of PCA is that the data is linearly separable. Kernal PCA, is a variant of PCA that can handle non-linear data and make it linearly separable. If you wonder what is linearly separable, Python Machine… Continue reading Introduction to Kernal PCA with Python