Linear Algebra for Data Science & Machine Learning A-Z 2023
Linear Algebra for Data Science & Machine Learning A-Z 2023 Course
Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra
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!
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What you’ll learn
- 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
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