Week 3 - BALT 4396b - Chapter 4
Introduction to Linear Algebra for Data Science
Linear Algebra is a part of mathematics that handles solving systems of linear equations. It deals with crucial concepts of Data Science such as vectors, matrices, and linear transformations. It is pretty important to acquire an understanding of Linear Algebra if you are working with data so that you are able to perform the data transformations. Assuming that the data/math you are inputting and working with is correct, you will manipulate the data in an efficient manner. The more advanced you get with your math skills the more you can dive into deeper learning models.
The basic building blocks of Linear Algebra:
The basic building blocks of Linear Algebra include the vectors, matrices, and the linear transformations. These building blocks allows for us to manipulate data in an organized approach. What is the function of each one of these concepts? Let's look at Vectors. Vectors are an organized list of numbers that take the place of different types of data "like coordinates in a space." Matrices (or a Matrix) are rectangular arrays of numbers. A rectangle consists of rows and columns and the numbers are inputting and organized into these.
Examples:
Vectors: [7,4]
Matrix: [8,5,9,0]
[7,6,1,3]
[4,9,6,2]
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