Gain insights into linear algebra essentials for data science, focusing on vectors, matrices, and tensors. Explore practical Python applications, engaging visuals, real datasets, and hands-on projects.

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Linear algebra is a fundamental pillar of data science. In advanced models in data science, like neural networks, the inputs and transformations are based upon vectors, matrices, and tensors which require a reasonable understanding of linear algebra to get the desired results. It is elegant and the most applied mathematics under the umbrella of data science.

This course teaches linear algebra with a focus on data science. This course encompasses several engaging illustrations, including static images and animations. Furthermore, this course presents mathematical modeling through programming in Python. This course contains several executable coding playgrounds on real data sets and a final project with practical applications.

Aside from theoretical implementations, the modern-day world needs its daunting calculations, trajectory mapping, and distance manipulation, all of which linear algebra provides. By the end of this course, you’ll have a working knowledge of all the necessary teachings in linear algebra.

Linear Algebra for Data Science Using Python

# Elimination
Elimination is a systematic process used for converting a matrix to one of its echelon forms. Depending on the form of this echelon matrix, the algorithm has a different variant. We’ll go through each variant one by one. We’ll use the following linear system as an example to explain the two variants of the elimination algorithm:

$$
\begin{array}{rcr}
-2x-3y+7z&=&10 \\
4x+8y-4z&=&4 \\
4x+9y-3z&=&8 \\ 2x+5y-z&=&6
\end{array}
$$

$$
\left(\begin{array}{rrr|r}
-2 & -3 & 7 & 10\\ 
4 & 8 & -4 & 4\\
4 & 9 & -3 & 8\\
2 & 5 & -1 & 6
\end{array}\right)
$$

 Learn the systematic approach to solving linear systems called Gaussian elimination.

Elimination

Learn the systematic approach to solving linear systems called Gaussian elimination.

Introduction

Linearity

Matrices

Solving Linear Systems

Singularity

Linear Regression and Least Squares

Vector Space

Vector Spaces of a Matrix

Singular Value Decomposition: SVD

Learning to Find Discriminative Null Space for Face Recognition

Gaussian Elimination

Elimination