In the previous post, we learned about PCA and how to use PCA in machine learning for various applications. In this tutorial, we are going to learn about linear discriminant analysis (LDA). In PCA we project the higher dimensional data to lower dimensions but we are only concerned with the features of the data in LDA we consider the classes of the data. This means LDA allow us to project data in a manner so that classes are linearly well separable. This means LDA is a supervised learning algorithm since we have labels involved here. So, let’s get started. If you have any questions, please ask in the forum/community support.
Master AI: Access In Depth Tutorials & Be Part Of Our Community.
We value the time and dedication in creating our comprehensive ML tutorials. To unlock the full in-depth tutorial, purchase or use your unlock credit. Your support motivates us to continue delivering high-quality tutorials. Thank you for considering – your encouragement is truly appreciated!