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

## Object
We’ve described vectors as arrays of numbers having two key operations of addition and scalar multiplication. However, in linear algebra, a vector may be generalized as an object that fulfills the linear function properties of additivity and homogeneity. Therefore, besides number arrays, matrices, and polynomials,  many other objects can also be taken as vectors if the operations of _addition_ and _scalar multiplication_ are defined for them.

Moreover, daily life objects, even with nonnumerical properties, can also be considered vectors. For example, _humans_ can be modeled as vectors that have the properties of hobby, age, and name. The addition of these properties can be defined as the concatenation of the names and the addition of the ages.


> **Note:** Like vectors, scalars also aren’t limited to real or complex numbers.

# Object
We’ve described vectors as arrays of numbers having two key operations of addition and scalar multiplication. However, in linear algebra, a vector may be generalized as an object that fulfills the linear function properties of additivity and homogeneity. Therefore, besides number arrays, matrices, and polynomials,  many other objects can also be taken as vectors if the operations of _addition_ and _scalar multiplication_ are defined for them.

Moreover, daily life objects, even with nonnumerical properties, can also be considered vectors. For example, _humans_ can be modeled as vectors that have the properties of hobby, age, and name. The addition of these properties can be defined as the concatenation of the names and the addition of the ages.


> **Note:** Like vectors, scalars also aren’t limited to real or complex numbers.

Learn the generality of vectors and the concept of operator overloading.

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

Vector Generalization

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