Algebra for Machine Learning Training Course

Course Code



14 hours (usually 2 days including breaks)


  • Basic experience or familiarity with machine learning
  • Basic programming experience


Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.

In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.

By the end of this training, participants will be able to:

  • Understand fundamental linear algebra concepts
  • Learn the linear algebra skills needed for machine learning
  • Use linear algebra structures and concepts when working with data, images, algorithms, etc.
  • Solve a machine learning problem using linear algebra


  • Developers
  • Engineers

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice


  • To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction to Linear Algebra

Why You Should Improve Your Linear Algebra Knowledge for Machine Learning

Learning Linear Algebra Notations

Understanding Vectors

  • Vector Properties and Characteristics
  • Performing Vector Operations

Understanding Matrices

  • Matrix Properties and Characteristics
  • Performing Matrix Operations and Transformations
  • Working with Special Matrices

Solving Linear Systems

  • Representing Problems as Linear Systems
  • Solving Linear Systems

Linear Mappings with Matrices

  • Orthogonal Matrices
  • The Gram-Schmidt Process

Reflecting and Manipulating Images with Matrices

Understanding Eigenvalues and Eigenvectors and their Application to Data Problems

Examining Google's PageRank Algorithm with Eigenvalues and Eigenvectors

Understanding Principal Components Analysis (PCA) for Machine Learning

Understanding Linear Regression for Machine Learning

Project: Solving a Machine Learning Problem with Linear Algebra

Summary and Conclusion



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