Linear Algebra and Probability: Fundamentals of Linear Algebra

Machine Learning
  • 13 Videos | 1h 45m 34s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

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