Linear Algebra for Data Science in R

In this chapter, you will learn about the key objects in linear algebra, such as vectors and matrices. You will understand why they are important and how they interact with each other.

Many machine learning algorithms boil down to solving a matrix-vector equation. In this chapter, you learn what matrix-vector equations are trying to accomplish and how to solve them in R.

Matrix operations are complex. Eigenvalue/eigenvector analyses allow you to decompose these operations into simpler ones for the sake of image recognition, genomic analysis, and more!

“Big Data” is ubiquitous in data science and its applications. However, redundancy in these datasets can be problematic. In this chapter, we learn about principal component analysis and how it can be used in dimension reduction.