Week 3
“It is not enough to have a good mind; the main thing is to use it well.”
– René Descartes
Linear Algebra lies at the core of data science. Simply put, linear algebra is the study of vectors, vector spaces, linear transformations (matrices). Large data is represented and manipulated in the form of matrices, more generally tensors for machine learning tasks. The study of linear algebra further facilitates rich techniques—such as LU Decomposition, Principal Component Analysis, Low-rank Approximation—to gain useful insights into data. The modern machine learning is only appreciated when the language of linear algebra. This chapter provides a gentle introduction to the building blocks of linear algebra: vectors and matrices.
In this chapter, we will learn the following:
Vectors, Matrices, and their operations
Numpyn-dimensional arraysManipulating
Numpyarrays