Explore the fundamentals of linear algebra, including characteristics and its role in machine learning, in this 13-video course. Learners can examine important concepts associated with linear algebra, such as the class of spaces, types of vector space, vector norms, linear product vector and theorems, and various operations that can be performed on matrix. Key concepts examined in this course include important classes of spaces associated with linear algebra; features of vector spaces and the different types of vector spaces and their application in distribution and Fourier analysis; and inner product spaces and the various theorems that are applied on inner product spaces. Next, you will learn how to implement vector arithmetic by using Python; learn how to implement vector scalar multiplication with Python; and learn the concept and different types of vector norms. Finally, learn how to implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication by using Python; and learn about matrix decomposition and the roles of Eigenvectors and Eigenvalues in machine learning.
Linear Algebra and Probability: Fundamentals of Linear Algebra
discover the key concepts covered in this course
identify the essential characteristics of linear algebra and its role in machine learning implementations
list the important classes of spaces associated with linear algebra
describe features of vector spaces and list the different types of vector spaces and their application in distribution and Fourier analysis
describe the concept of inner product spaces and the various theorems that are applied on inner product spaces
demonstrate how to implement vector arithmetic using Python
demonstrate how to implement vector scalar multiplication using Python
describe the concept and different types of vector norms
implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication using Python
recognize operations that can be performed on matrix, such as matrix norms and matrix identities
recognize how the trace, determinant, inverse, and transpose operations are applied on matrix
describe matrix decomposition, using eigendecomposition, and the role of Eigenvectors and Eigenvalues
describe the features of vector spaces, recall the different types of vector norms, and implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication using Python