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.

linearalgebra.tar.gz

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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

## Linear regression with several unknowns
So far, we've worked with examples of linear regression consisting of only one unknown. However, systems from real-life situations are typically complex, with several unknowns. In this lesson, we'll work with a more involved example of a #key# real data set:A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012 #key#.


## Context
The task at hand is to find a relationship that determines the amount of heating or cooling required by a building, given different _features_ of the building like roof area, wall area, and glazing.

The data set consists of 
 $8$ different _features_, namely X1 to X8, of $768$ different buildings, along with their heating and cooling loads, Y1 and Y2, respectively. Below is a representation of the first five data records and the header information.

# Linear regression with several unknowns
So far, we've worked with examples of linear regression consisting of only one unknown. However, systems from real-life situations are typically complex, with several unknowns. In this lesson, we'll work with a more involved example of a #key# real data set:A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012 #key#.


# Context
The task at hand is to find a relationship that determines the amount of heating or cooling required by a building, given different _features_ of the building like roof area, wall area, and glazing.

The data set consists of 
 $8$ different _features_, namely X1 to X8, of $768$ different buildings, along with their heating and cooling loads, Y1 and Y2, respectively. Below is a representation of the first five data records and the header information.

Learn to implement linear regression on a real data set with several features.

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

Linear Regression on Real Dataset

Linear regression with several unknowns

Context