# Mathematics for Machine Learning: Linear Algebra

Product is rated as #23 in category Data Science
 Learning experience 9.4 9.2

Throughout the course, we will learn, how linear algebra is relevant to machine learning and data science? We look at operations we can do with vectors. How to use matrices as tools to solve linear algebra problems. How matrices can transform a description of a vector from one basis (set of axes) to another, will allow us to apply a reflection to an image and manipulate images. Then we will go through special ‘Eigen-things’ that are very useful in linear algebra and will let us examine Google’s famous PageRank algorithm for presenting web search results.

Last updated on November 12, 2021 9:37 pm
Categories: Data Science, Mathematics Tags: Beginner, Course

In this course on Mathematics for Machine Learning, we take a look at what linear algebra is and how it associates with vectors and matrices. Then we check out what vectors and matrices are and how to deal with them, consisting of the knotty issue of eigenvalues and eigenvectors, and how to utilize these to resolve issues. Finally, we take a look at how to utilize these to do enjoyable things with datasets – like how to turn pictures of faces and how to draw out eigenvectors to take a look at how the Pagerank algorithm works.

Since we’re focusing on data-driven applications, we’ll be carrying out a few of these concepts in code, not simply on pencil and paper. Towards completion of the course, you’ll compose code blocks and encounter Jupyter note pads in Python, however, do not fret, these will be rather brief, concentrated on the principles, and will assist you through if you’ve not coded prior to.

At the completion of this course, you will have a user-friendly understanding of vectors and matrices that will assist you to bridge the space into linear algebra issues, and how to use these principles in machine learning.

## Syllabus:

This 5 weeks course has been organized by Imperial College of London, a world top ten university.

## Description

In this course on Mathematics for Machine Learning, we take a look at what linear algebra is and how it associates with vectors and matrices. Then we check out what vectors and matrices are and how to deal with them, consisting of the knotty issue of eigenvalues and eigenvectors, and how to utilize these to resolve issues. Finally, we take a look at how to utilize these to do enjoyable things with datasets – like how to turn pictures of faces and how to draw out eigenvectors to take a look at how the Pagerank algorithm works.

Since we’re focusing on data-driven applications, we’ll be carrying out a few of these concepts in code, not simply on pencil and paper. Towards completion of the course, you’ll compose code blocks and encounter Jupyter note pads in Python, however, do not fret, these will be rather brief, concentrated on the principles, and will assist you through if you’ve not coded prior to.

At the completion of this course, you will have a user-friendly understanding of vectors and matrices that will assist you to bridge the space into linear algebra issues, and how to use these principles in machine learning.

## Syllabus:

This 5 weeks course has been organized by Imperial College of London, a world top ten university.

## Specification:

• Coursera
• Imperial College London
• Online Course
• Self-paced
• Beginner
• Less Than 24 Hours
• Free Trial (Paid Course & Certificate)
• English
• Basic Maths
• Eigenvalues And Eigenvectors Linear Algebra Essentials Machine learning Transformation Matrix

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Mathematics for Machine Learning: Linear Algebra

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