The video tutorial explains how to calculate a similarity index using the linear kernel from the sklearn library. It guides through the process of developing a similarity matrix using TF-IDF matrices for self-similarity checking. Finally, it introduces the concept of building a recommendation engine based on the similarity matrix.

Explain the purpose of using the linear kernel in the context of the similarity matrix.

What is the first step to develop a similarity matrix according to the text?

What matrices are being compared to create the similarity matrix?

Describe the process of developing the similarity matrix as mentioned in the text.

What is the next important part after developing the similarity matrix?