English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 20m | 330 MB

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.

## Table of Contents

**Introduction**

1 Introduction

2 What you should know

**Introduction to Linear Algebra**

3 Defining linear algebra

4 Applications of linear algebra in ML

**Vectors Basics**

5 Introduction to vectors

6 Vector arithmetic

7 Coordinate system

**Vector Projections and Basis**

8 Dot product of vectors

9 Scalar and vector projection

10 Changing basis of vectors

11 Basis, linear independence, and span

**Introduction to Matrices**

12 Matrices introduction

13 Types of matrices

14 Types of matrix transformation

15 Composition or combination of matrix transformations

**Gaussian Elimination**

16 Solving linear equations using Gaussian elimination

17 Gaussian elimination and finding the inverse matrix

18 Inverse and determinant

**Matrices from Orthogonality to Gram–Schmidt Process**

19 Matrices changing basis

20 Transforming to the new basis

21 Orthogonal matrix

22 Gram–Schmidt process

**Eigenvalues and Eigenvectors**

23 Introduction to eigenvalues and eigenvectors

24 Calculating eigenvalues and eigenvectors

25 Changing to the eigenbasis

26 Google PageRank algorithm

**Conclusion**

27 Next steps

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