Let’s reiterate the learnings of the past two sessions:

- You understood the concept of basis vectors and how they’re helpful in the representation of data points.
- You then learnt about how you can use different basis vectors to represent the same information.
- Using the previous knowledge, you came to know that when represented under some ‘
**ideal basis vectors’**, it becomes easier for us to do dimensionality reduction. However, you didn’t exactly know how to find those ideal basis vectors. - Then you learnt about the concept of variance and how more variance meant more information.
- Then you derived that the more important columns in a dataset are the ones which capture more variance than the others.
- Subsequently, you deduced that the most important directions, rather than just columns, are those that
**capture maximum variance**. The ideal basis vectors that we talked about in the previous case are in fact those that do the same. - These basis vectors or directions that capture the maximum variance are essentially the
**Principal Components**for the dataset.

Report an error