linearalgebra.tar.gz

Code

visualizing-planes-3D

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convex-combinations-animation

intersecting planes

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

The goal of Gaussian elimination is to convert a matrix to its row echelon form, 
$ref$. We discussed the steps of the algorithm in detail in the [previous lesson](https://www.educative.io/courses/linear-algebra-for-data-science-using-python/echelon-matrices#Gaussian-elimination). In this lesson, we’ll translate those steps into a Python program.


## Implementation
The code below is an implementation of the Gaussian elimination algorithm in Python. The `Elimination` function takes a two-dimensional `np.array` as input. It performs the elimination while printing each step and returns the $ref$ of the input array as output.


The goal of Gaussian elimination is to convert a matrix to its row echelon form, 
$ref$. We discussed the steps of the algorithm in detail in the [previous lesson](https://www.educative.io/courses/linear-algebra-for-data-science-using-python/echelon-matrices#Gaussian-elimination). In this lesson, we’ll translate those steps into a Python program.


# Implementation
The code below is an implementation of the Gaussian elimination algorithm in Python. The `Elimination` function takes a two-dimensional `np.array` as input. It performs the elimination while printing each step and returns the $ref$ of the input array as output.


Implement Gaussian elimination in Python.

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 Using Python

Implementation