# Become a Linear Algebra Master

Udemy Coupon - Become a Linear Algebra Master, Learn everything from Linear Algebra, then test your knowledge with 400+ practice questions

- Bestseller
- Created by Krista King
- English [Auto], French [Auto]

**What you'll learn**

- Operations on one matrix, including solving linear systems, and Gauss-Jordan elimination
- Operations on two matrices, including matrix multiplication and elimination matrices
- Matrices as vectors, including linear combinations and span, linear independence, and subspaces
- Dot products and cross products, including the Cauchy-Schwarz and vector triangle inequalities
- Matrix-vector products, including the null and column spaces, and solving Ax=b
- Transformations, including linear transformations, projections, and composition of transformations
- Inverses, including invertible and singular matrices, and solving systems with inverse matrices
- Determinants, including upper and lower triangular matrices, and Cramer's rule
- Transposes, including their determinants, and the null (left null) and column (row) spaces of the transpose
- Orthogonality and change of basis, including orthogonal complements, projections onto a subspace, least squares, and changing the basis
- Orthonormal bases and Gram-Schmidt, including definition of the orthonormal basis, and converting to an orthonormal basis with the Gram-Schmidt process
- Eigenvalues and Eigenvectors, including finding eigenvalues and their associate eigenvectors and eigenspaces, and eigen in three dimensions

**Description**

HOW BECOME A LINEAR ALGEBRA MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:

This 247-lesson course includes video and text explanations of everything from Linear Algebra, and it includes 69 quizzes (with solutions!) and an additional 12 workbooks with extra practice problems, to help you test your understanding along the way. Become a Linear Algebra Master is organized into the following sections:

Operations on one matrix, including solving linear systems, and Gauss-Jordan elimination

Operations on two matrices, including matrix multiplication and elimination matrices

Matrices as vectors, including linear combinations and span, linear independence, and subspaces

Dot products and cross products, including the Cauchy-Schwarz and vector triangle inequalities

Matrix-vector products, including the null and column spaces, and solving Ax=b

Transformations, including linear transformations, projections, and composition of transformations

Inverses, including invertible and singular matrices, and solving systems with inverse matrices

Determinants, including upper and lower triangular matrices, and Cramer's rule

Transposes, including their determinants, and the null (left null) and column (row) spaces of the transpose

Orthogonality and change of basis, including orthogonal complements, projections onto a subspace, least squares, and changing the basis

Orthonormal bases and Gram-Schmidt, including definition of the orthonormal basis, and converting to an orthonormal basis with the Gram-Schmidt process

Eigenvalues and Eigenvectors, including finding eigenvalues and their associate eigenvectors and eigenspaces, and eigen in three dimensions

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.

Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

HERE'S WHAT SOME STUDENTS OF BECOME A LINEAR ALGEBRA MASTER HAVE TOLD ME:

“Another fantastic course. Provides an academic foundation of linear algebra to prepare for applied or programming-based courses.” - Christopher C.

“I have no words to thank Krista for this amazing course, I was really overwhelmed because I had to take a test for a class I couldn't attend and I didn't know anything about linear algebra and surprisingly this course was what I needed, reading the notes before watching the video helped to understand by myself and when I was lost the video content was a great resource, I got a 9 out of 10 in the test, so I highly recommend to take this course, Krista is such a good teacher.” - Alan M.

“I started out as a math major in college, and dropped my major during linear algebra. I wish I had this class and this instructor in college. I might have stuck with my major.” - Eric L.

“Notes are great, explanations are clear and starting from the beginning. Terrific so far.” - Phil T.

“Very clear and has not skipped any steps. If the rest of the course is like this, I will pass my class with no problem.” - Brandon P.

“Really well structured and well explained, and there are plenty of exercises to reinforce the knowledge.” - Ashfaque C.

YOU'LL ALSO GET:

Lifetime access to Become a Linear Algebra Master

Friendly support in the Q&A section

Udemy Certificate of Completion available for download

30-day money back guarantee

Enroll today!

I can't wait for you to get started on mastering Linear Algebra.

- Krista :)

Who this course is for:

Current Linear Algebra students, or students about to start Linear Algebra who are looking to get ahead

Anyone who wants to study math for fun after being away from school for a while

Anyone who needs Linear Algebra as a prerequisite for Machine Learning, Deep Learning, Artificial Intelligence, Computer Programming, Computer Graphics and Animation, Data Analysis, etc.

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