Machine Learning Foundations: Linear Algebra

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Terezija Semenski

1:20:12

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01 - Introduction

01 - Introduction.mp4

01:05

02 - What you should know.mp4

01:04

02 - 1. Introduction to Linear Algebra

01 - Defining linear algebra.mp4

02:22

02 - Applications of linear algebra in ML.mp4

05:04

03 - 2. Vectors Basics

01 - Introduction to vectors.mp4

04:47

02 - Vector arithmetic.mp4

03:58

03 - Coordinate system.mp4

03:06

04 - 3. Vector Projections and Basis

01 - Dot product of vectors.mp4

03:10

02 - Scalar and vector projection.mp4

02:48

03 - Changing basis of vectors.mp4

04:16

04 - Basis, linear independence, and span.mp4

02:54

05 - 4. Introduction to Matrices

01 - Matrices introduction.mp4

02:44

02 - Types of matrices.mp4

02:46

03 - Types of matrix transformation.mp4

02:51

04 - Composition or combination of matrix transformations.mp4

02:55

06 - 5. Gaussian Elimination

01 - Solving linear equations using Gaussian elimination.mp4

04:00

02 - Gaussian elimination and finding the inverse matrix.mp4

03:06

03 - Inverse and determinant.mp4

02:58

07 - 6. Matrices from Orthogonality to Gram-Schmidt Process

01 - Matrices changing basis.mp4

02:09

02 - Transforming to the new basis.mp4

02:48

03 - Orthogonal matrix.mp4

02:10

04 - Gram-Schmidt process.mp4

02:52

08 - 7. Eigenvalues and Eigenvectors

01 - Introduction to eigenvalues and eigenvectors.mp4

02:36

02 - Calculating eigenvalues and eigenvectors.mp4

03:09

03 - Changing to the eigenbasis.mp4

03:58

04 - Google PageRank algorithm.mp4

03:57

09 - Conclusion

01 - Next steps.mp4

00:39

Description

Ever wondered what’s really going on underneath a machine learning algorithm? The answer is linear algebra. Without it, machine learning can’t exist. Linear algebra is a prerequisite for understanding and creating nearly all machine learning algorithms, especially those that prop up neural networks, natural language processing tools, and deep learning models.

Join instructor Terezija Semenski for an in-depth exploration of the core concepts of linear algebra alongside the techniques needed to design and implement a successful machine learning algorithm. Discover the basics of vector arithmetic, vector norms, matrix properties, advanced operations, matrix transformation, and algorithms like Google PageRank. By the end of this course, you’ll be ready to take the principles of linear algebra and apply them to your next big machine learning project.

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Machine Learning

Linear Algebra

Terezija Semenski

Instructor's Courses

I help busy people learn to code without spending hours on searching Google and Stackoverflow. Mathematician and Software Developer with a business mind, a learning mindset, and a passion for people. Currently working as a freelance Educator and Software Developer. I’m also preparing my 8th online course. My previous experience includes working as Software Developer and QA in startup on educational, financial and banking app development projects and in the education sector as IT and Mathematics Teacher.

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.