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Linear Algebra for Machine Learning – The Easy Way

Linear algebra is the branch of mathematics concerning linear equations. It is the fundamental to geometry, for defining objects such as lines, planes, rotations. In this course we will discuss the Concepts of linear algebra needed for ML.

365 days course access

Live instructor-led online classes

Industry-based projects

Master the Linear Algebra Concepts used in Machine Learning.

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Includes:

  • 2 hours of lecture Videos
  • 20 hands-on practice exercises
  • 27 Assessment exercises
  • 5 code analysis exercises
  • 300 knowledge based questions
  • 2 Live connect sessions
             (Master classes)
  • Lifetime access
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Linear Algebra for Machine Learning – The Easy Way

ABOUT THE COURSE

This course helps you to understand the vectors in different dimensions and their linear transformation to some other domain, matrix theory and various properties of matrices and its transformations, Eigenvalues and Eigenvectors and their role in dimensionality reduction and the applications of linear algebra for AI and ML.

COURSE OBJECTIVES

Upon successful completion of the course, the learner will be able to :
  • Master the various concepts in Linear Algebra.
  • Learn how linear algebra plays an important role in reducing the complexity of the data processing in AI and ML algorithms.

Course Content

Vector spaces, Linear Transformations

In this topic, you will learn about the multidimensional vector space and its various representations. You will also understand the transformations between different domains.

  • 1 Video
  • 3 hours
  • 65 Problems

Subspaces, Span and Basis

In this topic, you will learn about Subspaces, span and Basis of a vector and how it reduces the complexity of representing the vectors.

  • 1 Video
  • 3 hours
  • 65 Problems

Matrix Theory

In this topic, you will learn about Grouping multiple vectors to form matrices and various types of matrices and its properties.

  • 1 Video
  • 3 hours
  • 65 Problems

Eigenvalues and Eigenvectors

In this topic, you will learn about how to derive Eigenvalues and Eigenvectors from the matrices and how they are used to reduce dimension.

  • 1 Video
  • 3 hours
  • 65 Problems

Inner-Product Spaces, Linear Algebra Applications for ML & AI

In this topic, you will learn about how to find the inner product of the matrix and its properties and short cuts to find it. You will also learn how Linear Algebra is used in the field of AI and ML.

  • 1 Video
  • 10 hours
  • 92 Problems

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