Save for later

Mathematics for Machine Learning

Linear Algebra

This course is a part of Mathematics for Machine Learning, a 3-course Specialization series from Coursera.

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.

Get Details and Enroll Now

OpenCourser is an affiliate partner of Coursera.

Set Reminder Save for later

Get a Reminder

Not ready to enroll yet? We'll send you an email reminder for this course

Send to:

Coursera

&

Imperial College London

Rating 4.5 based on 466 ratings
Length 6 weeks
Effort 5 weeks of study, 2-5 hours/week
Starts Sep 16 (9 weeks ago)
Cost $49
From Imperial College London via Coursera
Instructors David Dye, Samuel J. Cooper, A. Freddie Page
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming Mathematics
Tags Data Science Machine Learning Math And Logic

Get a Reminder

Get an email reminder about this course

Send to:

What people are saying

According to other learners, here's what you need to know

programming assignment in 15 reviews

Overall good, but some nasty difficulty with the programming assignments... especially the last one.

Programming assignments failed to save and submit sometimes.

Other than the first 2-3 intuition videos and the programming assignment nothing was good in the 5th module/week.

Maybe the programming assignments are far too easy, while some of the quizzes definitely are hard.

The programming assignment do require previous Python/other programming experience.

This course is phenomenal, It helped me to refresh a lot of skills that I learned at my college and at the same time I learned a bit on how to introduce all this matrixes into a programming assignment which are by the way extremely hard because I am a novice at programming.

Although I would have preferred more challenging quizzes and programming assignments the material taught was still world class.

Read more

data science in 14 reviews

I audited the course to gain practical experience and notation reading skills for my data science studies.

Overall, loved this course and highly recommend it to data science enthusiasts taking baby steps towards deep learning.

I would give this course 5 stars for the fact that in five weeks, the course is able to go through perhaps a semester or two or three of Linear Algebra (LA), and how LA fits into data science.

Because I had done a couple other courses on LA relatively recently, some these arcane LA concepts were grasped with some, but not too much, effort.If you are even just a little familiar with LA, this course will give you a good foundation for the LA relative to data science.

(I'm an old-timer, reviewing this material to get up to speed on Machine Learning and Data Science.)

This course can help anyone build a good foundation in Linear Algebra very nice It's a great foundation course for anyone who wants start their journey in Data Science.The content is relevant to ML applications.

The course for every engineer who want to refresh math skills before trying data science.

Read more

for machine learning in 12 reviews

This course is very good to build your fundamental knowledge for machine learning.

Provides a good understanding of Linear Algebra for Machine Learning.

Good material if you want to refresh your knowledge, poor programming assignment support/feedback This is an excellent course for Machine learning foundation.

Mathematics for Machine Learning: Linear Algebra ... REVISED I really liked the pace of this course.

A great course to learn mathematics for machine learning .

If you're looking at refreshing your knowledge of linear algebra for machine learning, this is good course to take.

A good course for gaining knowledge for Linear Algebra for machine learning.

Read more

easy to understand in 8 reviews

Easy to understand material and instructor is great.

I took a great pleasure to study this linear algebra course, teachers are very talented since their way to explain mathematical concepts make it very easy to understand , in fact with this particular amazing approach I changed my perception about learning math and sciences in general.

The best linear algebra courses I ever learnt! easy to understand A great introduction to linear algebra!

This course makes the Linear Algebra very easy to understand.

Great course Great teacher, great course, easy to understand but still challenging.

The interpretations given for matrix multiplication and change of basis are presented in simple terms which are easy to understand.

The linear algebra was taught in an easy to understand manor but the applications in machine learning were quite sparse This course is a must for all the people who wants to go deep into machine learning and data science as this covers the prerequisites of the courses available.

Read more

rather than in 6 reviews

This course is much more focused on the meaning and usefulness of these things, rather than just learning how to do the maths.

The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics.

Great Course, exceptional in every way, gives you practice drill down some of the concepts, and handy programming assignments that are fun to work with, while not a complete refresher the course is good enough to grasp essence of linear algebra to build intuitive math, rather than classical way of teaching.

Thus the course should be considered a brief glance at linear algebra, rather than a proper course on the subject.

Focused on the geometrical view to look at the linear algebra rather than hand-calculations.

Read more

matrices and vectors in 5 reviews

This course changed the view I look at matrices and vectors.

Mainly explains how to operate with matrices and vectors.

Great lectures and wonderful scrutiny of matrices and vectors.

It finally starts making sense why we use matrices and vectors.

Read more

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

Write a review

Your opinion matters. Tell us what you think.

Coursera

&

Imperial College London

Rating 4.5 based on 466 ratings
Length 6 weeks
Effort 5 weeks of study, 2-5 hours/week
Starts Sep 16 (9 weeks ago)
Cost $49
From Imperial College London via Coursera
Instructors David Dye, Samuel J. Cooper, A. Freddie Page
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming Mathematics
Tags Data Science Machine Learning Math And Logic

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now