Linear Algebra and Probability: Fundamentals of Linear Algebra

Machine Learning    |    Intermediate
  • 13 videos | 1h 40m 4s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 235 users Rating 4.3 of 235 users (235)
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

  • 1m 39s
  • 15m 7s
    In this video, find out how to identify the essential characteristics of linear algebra and its role in machine learning implementations. FREE ACCESS
  • Locked
    3.  Class of Spaces
    7m 41s
    Upon completion of this video, you will be able to list the important classes of spaces associated with linear algebra. FREE ACCESS
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    4.  Types of Vector Space
    9m 39s
    Upon completion of this video, you will be able to describe features of vector spaces and list the different types of vector spaces and their applications in distribution and Fourier analysis. FREE ACCESS
  • Locked
    5.  Linear Product Vector and Theorems
    7m 32s
    After completing this video, you will be able to describe the concept of inner product spaces and the various theorems that apply to inner product spaces. FREE ACCESS
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    6.  Vector Arithmetic
    5m 50s
    In this video, you will learn how to implement vector arithmetic using Python. FREE ACCESS
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    7.  Vector Scalar Multiplication
    3m 16s
    In this video, you will learn how to implement vector scalar multiplication using Python. FREE ACCESS
  • Locked
    8.  Vector Norms
    7m 37s
    Upon completion of this video, you will be able to describe the concept of vector norms and different types of vector norms. FREE ACCESS
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    9.  Matrix Arithmetic
    5m 54s
    In this video, learn how to implement matrix-matrix multiplication, matrix-vector multiplication, and matrix-scalar multiplication using Python. FREE ACCESS
  • Locked
    10.  Working with Matrix
    12m 27s
    Upon completion of this video, you will be able to recognize operations that can be performed on a matrix, such as matrix norms and matrix identities. FREE ACCESS
  • Locked
    11.  Matrix Operations
    9m 29s
    Upon completion of this video, you will be able to recognize how to apply the trace, determinant, inverse, and transpose operations on a matrix. FREE ACCESS
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    12.  Matrix Decomposition
    7m 44s
    Upon completion of this video, you will be able to describe matrix decomposition using eigendecomposition, and the role of eigenvectors and eigenvalues. FREE ACCESS
  • Locked
    13.  Exercise: Vector Norms and Matrix Arithmetic
    6m 9s
    After completing this video, you will be able to describe the features of vector spaces, recall the different types of vector norms, and implement matrix-matrix multiplication, matrix-vector multiplication, and matrix-scalar multiplication using Python. FREE ACCESS

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