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Video Description:
Welcome to this video on PCA Eigen Vectors and Eigen Values in Machine Learning. In this tutorial, we will be discussing these concepts in detail, along with some practical examples to help you understand how they are used in machine learning.

Eigen vectors and eigen values are important concepts in machine learning and data analysis. Eigen vectors are used to project data onto a lower dimensional space, while eigen values are used to determine the importance of each eigen vector.

PCA (Principal Component Analysis) is a popular technique used in machine learning to reduce the dimensions of a dataset. It does this by finding the eigen vectors and eigen values of the data and then projecting the data onto the lower dimensional space defined by the eigen vectors.

In this video, we will be explaining the mathematical concepts behind eigen vectors and eigen values, and how they are used in PCA. We will also be demonstrating how to implement PCA in Python using the scikit-learn library.

Whether you are a beginner or an experienced machine learning practitioner, this video will provide you with a solid understanding of these concepts and how to apply them in your own projects.

We hope you find this tutorial helpful. Thank you for watching!
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