 # Complete Linear Algebra for Data Science & Machine Learning

### Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra

#### What you’ll learn

Complete Linear Algebra for Data Science & Machine Learning Course Site

• Fundamentals of Linear Algebra and how to ace your Linear Algebra exam
• Basics of matrices (notation, dimensions, types, addressing the entries, etc.)
• Operations on a single matrix, e.g. scalar multiplication, transpose, determinant & adjoint
• Operations on two matrices, including addition, subtraction and multiplication of matrices
• Performing elementary row operations and finding Echelon Forms (REF & RREF)
• Inverses, including invertible and singular matrices, and the Cofactor method
• Solving systems of linear equations using matrices and inverse matrices, including Cramer’s rule to solve AX = B
• Properties of determinants, and how to perform Gauss-Jordan elimination
• Matrices as vectors, including vector addition and subtraction, Head-to-Tail rule, components, magnitude and midpoint of a vector
• Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms
• Linear combinations and span, spanning set for a vector space and linear dependence
• Subspace and Null-space of a matrix, matrix-vector products
• Basis and standard basis, and checking if a set of given vectors forms the basis for a vector space
• Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors
• Basic algebra concepts ( as a BONUS)
• And so much more…..

#### Requirements

• A passion to learn about Matrices and Vectors
• Ability to perform basic mathematical operations (+, -, x, ÷) on numbers and fractions
• Knowledge of how to solve a linear equation (e.g. find x in 3x-4=11)
• Understanding of basic Algebra concepts, e.g. Powers and Roots, simplifying Fractions, Factorization, solving Equations and drawing Graphs.
• You only need to know basic Math and Algebra to take this course.
• And the best thing is, most of the above prerequisite topics are covered inside the course ?

#### Description

DO YOU WANT TO LEARN LINEAR ALGEBRA IN AN EASY WAY?

Great!

With 22+ hours of content and 200+ video lessons, this course covers everything in Linear Algebra, from start till the end!

Every concept is explained in simple language, and Quizzes and Assignments (with solutions!) help you test your concepts as you proceed.

#### Whether you’re a student, or a professional or a Math enthusiast, this course walks you through the core concepts of Linear Algebra in an easy and fun way!

HERE IS WHAT YOU WILL LEARN:

Fundamentals of Linear Algebra and how to ace your Linear Algebra exam

Basics of matrices, including notation, dimensions, types, addressing the entries, etc.

Operations on a single matrix, e.g. scalar multiplication, transpose, determinant, adjoint, etc.

Operations on two matrices, including addition, subtraction and multiplication

Performing elementary row operations and finding Echelon Forms (REF & RREF)

Inverses, including invertible and singular matrices, and the Cofactor method

Solving systems of equations using matrices & inverse matrices, including Cramer’s rule to solve AX = B

#### Performing Gauss-Jordan elimination

Properties of determinants and how to utilize them to gain insights

Matrices as vectors, including vector addition and subtraction, Head-to-Tail rule, components, magnitude and midpoint of a vector

Linear combinations of vectors and span

Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms

Subspace and Null-space of a matrix, matrix-vector products

Spanning set for a vector space and linear dependence

Basis and standard basis, and checking if a set of given vectors forms the basis for a vector space

Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors

Basic algebra concepts (as a BONUS)

And so much more…..

Video Lessons:

#### Watch over my shoulder as I explain all the Linear Algebra concepts in a simple and easy to understand language.

This problem-based approach is great, especially for beginners who want to practice their math concepts while learning.

Quizzes: When you think you have understood a concept well, test it by taking the relevant quiz. If you pass, awesome! Otherwise, review the suggested lessons and retake the quiz, or ask for help in the Q/A section.

Assignments: Multiple assignments offer you a chance for additional practice by solving sets of relevant and insightful problems (with solutions provided)

By the end of this course, you’ll feel confident and comfortable with all the Linear Algebra topics discussed in this course!

#### WHY SHOULD YOU LEARN LINEAR ALGEBRA?

• Linear Algebra is a prerequisite for many lucrative careers, including Data Science, Artificial Intelligence, Machine Learning, Financial Math, Data Engineering, etc.
• Being proficient in Linear Algebra will open doors for you to many high-in-demand careers

WHY LEARN LINEAR ALGEBRA FROM ME?

I took this Linear Algebra class at the University of Illinois at Urbana Champaign, one of the Top-5 Engineering Schools in the country, and I have tried to follow the same standards while designing this course.

My goal is to make this the best Linear Algebra and Math course online, and I’ll do anything possible to help you learn.

YOU’LL ALSO GET:

Friendly support in the Q&A section

ENROLL TODAY!

Feel free to check out the course outline below or watch the free preview lessons. Or go ahead and enroll now.

Cheers,

Kashif

#### Who this course is for:

• Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it
• Professionals who need a refresher in Math, especially Algebra and Linear Algebra
• Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces
• Anyone who wants to master Linear Algebra for Data Science, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, Computer Graphics, Programming, etc.
• Last updated 2/2020    