Linear Algebra for Data Science Using Python

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.

Beginner

67 Lessons

10h

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project
170 Playgrounds
8 Challenges
9 Quizzes

This course includes

1 Project
170 Playgrounds
8 Challenges
9 Quizzes

Course Overview

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 a...Show More

What You'll Learn

Learning the intricate concepts of linear algebra from scratch

Working knowledge of various linear algebra techniques using Python

A visual understanding of concepts such as vector space, spans, and subspace with animations

Familiarity with valuable concepts like fields, eigenspaces, diagonalization, and SVD

An understanding of how linear algebra concepts build the most useful tools in data science, such as neural networks

The ability to apply linear algebra concepts to real-world problems through coding exercises and practical projects

What You'll Learn

Learning the intricate concepts of linear algebra from scratch

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Course Content

1.

Introduction

Get familiar with linear algebra applications in data science using Python.
2.

Linearity

Get started with linear functions, linear combinations, and solving linear systems in data science.
3.

Matrices

Master the steps to utilize matrices and perform matrix operations essential for data science.
4.

Solving Linear Systems

Grasp the fundamentals of solving linear systems, Gaussian elimination, and matrix rank.
5.

Singularity

Map out the steps for working with matrices in data science using elementary transformations.
6.

Linear Regression and Least Squares

11 Lessons

Focus on linear and non-linear regression techniques, practical applications, multi-target regression, and neural networks.
7.

Vector Space

12 Lessons

Build on vector properties, sets, fields, vector spaces, subspaces, and applications in data science.
8.

Vector Spaces of a Matrix

5 Lessons

Step through vector spaces, null spaces, orthogonal complements, and eigenspaces in matrix algebra.
9.

Singular Value Decomposition: SVD

3 Lessons

Get started with orthogonal diagonalization and Singular Value Decomposition (SVD) for matrix factorization.

Learning to Find Discriminative Null Space for Face Recognition

Project

Trusted by 1.4 million developers working at companies

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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