Back to First Steps in Linear Algebra for Machine Learning

4.2

stars

50 ratings

•

9 reviews

The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.
This course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well....

Jul 25, 2020

with great assignments,one could get relation of LA with computer science.For naive english it could hurdles at first.but nice content.must for intermediatory

Mar 01, 2020

The material was very well presented, and the exercises were helpful for learning

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By Alagu P P G

•Jul 25, 2020

with great assignments,one could get relation of LA with computer science.For naive english it could hurdles at first.but nice content.must for intermediatory

By k v r

•Apr 15, 2020

poor presenation

By Xingxing T

•Apr 09, 2020

The program is really useful for exploring machine learning. I appreciate that the math involved are so relevant to SVM, even though I am BA major and do not have strong math background. I just need put enough time and go through each example. That's where I find this course very suitable. Plenty examples to explain the concepts.

By Carlos M V R

•Jul 21, 2020

This is a great courses, sometimes explanations could be better but in general is awesome and they teach us good applications of linear algebra in the field of machine learning. I would like to rate this course with 4.5, but Coursera does not allow us to rate in that way.

By Ruthlyn N V

•Jun 05, 2020

The programming assignments were really challenging! I thought I'm not gonna pass this course since it's my first time to encounter Python language. Thank you so much Prof. Piontkovski and Prof. Chernyshev for the new learnings :)

By Kevin A G D

•Aug 08, 2020

I learnt a lot with this course, good introduction to linear algebra, and good guidance to the use of sk-learn for Machine Learning.

By Daniel H C

•Mar 01, 2020

The material was very well presented, and the exercises were helpful for learning

By Tan Z

•Apr 25, 2020

Good, but have few examples and exercises.

By Roger S

•Feb 26, 2020

Very conventional and theoretical way of presenting the stuff. There are some Python exercises, though.

Not much course materials.

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