Become a Linear Algebra Master– Udemy

Become a Linear Algebra Master– Udemy ranks 2nd in the list of best online Linear Algebra courses. This linear algebra course is not dedicated to data science learners but covers all required linear algebra topics for data science. In this course, you will learn Matrices as vectors, Matrix-vector products, Inverses, Transposes, etc. 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.

The most important things to remember are found in the notes section of each lesson. It's similar to Cliff Notes, but for math. Nothing you don't need to know in order to pass your class. When you believe you have a good understanding of a course topic, you can put your knowledge to the test by taking one of the quizzes. Congratulations! If not, you can go over the videos and notes again, or you can ask for assistance in the Q&A section. Do you need more practice? When you've finished the section, go through the bonus workbook to review everything you've learned. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

What you will 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

Extra Benefits:

• You will get a Certificate of completion.
• Along with this, you will get 171 downloadable resources and 98 articles.

Who Should Enroll?

• Those who know linear algebra basics.

Rating: 4.8/5.0

Enroll here: udemy.com/course/linear-algebra-course