Free online courseLinear Algebra for Machine Learning

Duration of the online course: 6 hours and 57 minutes

New course

Explore linear algebra's vital role in machine learning with this comprehensive free online course. Perfect for AI enthusiasts. Enroll now to start learning!

Course Description

Dive into the world of linear algebra with this comprehensive free online course tailored for machine learning enthusiasts. This course serves as an essential foundation for anyone looking to explore the intricacies of artificial intelligence. It offers a robust introduction to the fundamentals of linear algebra and its pivotal role in machine learning.

Begin your journey with an overview of machine learning foundations, and get acquainted with the concept of linear algebra. Gain insights through plotting systems of linear equations and engage in practical exercises to reinforce your learning. Expand your understanding by exploring the realm of tensors, scalars, vectors, and vector transposition.

Develop proficiency in norms and unit vectors, and explore the significance of basis, orthogonal, and orthonormal vectors. Master matrix tensors and get comfortable with generic tensor notation. Enjoy hands-on exercises on algebra data structures, and delve into essential tensor operations including the Hadamard product and tensor reduction. The course brings clarity to solving linear systems through substitution and elimination, with bonus insights offered through video content.

Progress further into understanding matrix properties, including the Frobenius norm, matrix multiplication, and the intricacies of symmetric and identity matrices. The course provides detailed exercises on matrix multiplication and challenges your understanding through matrix inversion, diagonal matrices, and orthogonal matrices.

Advance to more complex topics with the second segment focused on matrix operations, including applying matrices, affine transformations, and the exploration of eigenvectors and eigenvalues. Delve into matrix determinants and their applications, and gain expertise in eigendecomposition. Practical applications of eigenvectors and eigenvalues in machine learning are thoroughly covered.

Conclude your learning journey by exploring singular value decomposition and its applications in data compression. Further your understanding with the Moore-Penrose pseudoinverse and its role in regression analysis. Explore advanced topics such as the trace operator and principal component analysis, ensuring a well-rounded grasp of linear algebra for machine learning.

Course content

  • Video class: Machine Learning Foundations: Welcome to the Journey

    0h02m

  • Video class: What Linear Algebra Is — Topic 1 of Machine Learning Foundations

    0h24m

  • Video class: Plotting a System of Linear Equations — Machine Learning Foundations Bonus Video

    0h09m

  • Video class: Linear Algebra Exercise — Topic 2 of Machine Learning Foundations

    0h02m

  • Video class: Tensors — Topic 3 of Machine Learning Foundations

    0h02m

  • Video class: Scalars — Topic 4 of Machine Learning Foundations

    0h13m

  • Video class: Vectors and Vector Transposition — Topic 5 of Machine Learning Foundations

    0h12m

  • Video class: Norms and Unit Vectors — Topic 6 of Machine Learning Foundations

    0h15m

  • Video class: Basis, Orthogonal, and Orthonormal Vectors — Topic 7 of Machine Learning Foundations

    0h04m

  • Video class: Matrix Tensors — Topic 8 of Machine Learning Foundations

    0h08m

  • Video class: Generic Tensor Notation — Topic 9 of Machine Learning Foundations

    0h06m

  • Video class: Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations

    0h00m

  • Video class: Tensor Operations — Segment 2 of Subject 1, "Intro to Linear Algebra", ML Foundations

    0h01m

  • Video class: Tensor Transposition — Topic 11 of Machine Learning Foundations

    0h03m

  • Video class: Basic Tensor Arithmetic (The Hadamard Product) — Topic 12 of Machine Learning Foundations

    0h06m

  • Video class: Tensor Reduction — Topic 13 of Machine Learning Foundations

    0h03m

  • Video class: The Dot Product — Topic 14 of Machine Learning Foundations

    0h05m

  • Video class: Exercises on Tensor Operations — Topic 15 of Machine Learning Foundations

    0h00m

  • Video class: Solving Linear Systems with Substitution — Topic 16 of Machine Learning Foundations

    0h04m

  • Video class: Solving Linear Systems with Elimination — Topic 17 of Machine Learning Foundations

    0h05m

This free course includes:

6 hours and 57 minutes of online video course

Exercises to train your knowledge

Certificate of course completion

100% free, from content to certificate

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