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Back to Mathematics for Machine Learning: Linear Algebra

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11,458 ratings

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

PL

Aug 25, 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

HE

Aug 8, 2021

the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering

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By 20131A4237 N V D B S

•May 28, 2022

i have experienced mathematics into machine learning coding ..i loved it..!

By Teiichi A

•Aug 5, 2021

A simpler course within the specilization. I enjoyed a lot, nevertheless.

By EDDY E A P

•Sep 4, 2020

Very well explained course and the exercises are very good and challenging.

By Maria C L C

•Jul 9, 2020

Amazing course overall! Great instructors, great and challenging exercises!

By Merrill N

•Oct 3, 2019

An excellent course to learn the geometric intuition behind linear algebra.

By Charles E

•May 5, 2018

Great short intro to linear algebra. Excellent treatment of eigen problems.

By Stefan B

•Oct 31, 2021

Excellent course with excellent lecturers, great start in machine learning

By Sridevi U

•Jun 29, 2020

Dear Professor, Very Good Course to learn mathematics for machine learning

By BENSAFOUAN A

•Jun 19, 2020

Excelent course , this is my best course in Linear Algebra i really likeit

By Deleted A

•Nov 11, 2018

Exceptional instructors! A lot of practical application and use of python.

By Will T

•Nov 17, 2021

Really Excellent, was able to gain insight with only and A Level in Maths

By Aaron C

•Sep 15, 2020

Very well presented, blew the cobwebs off my brain since Math@UCL(89) :)

By Himanshu A

•Jun 29, 2020

a good beginner course for linear algebra. basics are taught intuitively.

By Ayan S 1

•Jun 9, 2020

It was really good, with the relevant topics related to machine learning.

By Kaustubh D

•May 1, 2020

A very very good course with clear explanation and best quality teachers.

By Jessica S P

•Apr 28, 2020

Very useful. Teaching was very clear for me. It was more than I expected.

By Katya G

•Apr 12, 2020

Very nice course that helps to build intuition in linear algebra. Thanks!

By Bernardo M S

•Aug 17, 2018

Greate explanation in general. Exercises were concise as well as lectures

By Jeong-min S

•Sep 6, 2021

it was very helpful and good to understanding Linear Algebra. Thank you.

By SAMYAK K S

•Apr 20, 2021

Worth taking course. It will give the required insights for a beginner.

By Syahril A G

•May 16, 2020

Great course ! I've learn this topic more than everythin in just 3 days.

By Deleted A

•Apr 7, 2020

Good explanation, earned new knowledge, definitely recommend this course

By Pranav P

•Apr 4, 2020

I loved the way tutors explained the concept. It was an nice experience.

By LUK K L

•Aug 31, 2019

This is an intensive but very useful course of stepping stone to ML Math

By Tomáš R

•Feb 10, 2019

perfect recap of necessary terminology and math algorithms, great course

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