Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.
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.
автор: Siddharth S•
A nice course.
автор: ayush p•
автор: Akhil K•
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автор: Luciano M•
автор: Zhassulan S•
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автор: Reed R•
The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e. (a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools. While I appreciate this goal and enjoyed the early foray into projection, I never felt the "a ha" moments I did as an undergrad in a class that used Gil Strang's "Introduction to Linear Algebra" (which I reread alongside this course as a supplement). The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course. It could be that I was just looking for a more complete treatment of the subject than the course ever intends to offer, but I strongly felt that with a bit of restructuring, that the subject could be presented primarily intuitively, but with a level of clarity and artfulness in its conclusion that will ensure that students remember the core concepts beyond when they remember its presentation.
автор: Eitan A•
As of this writing, I am almost done with week 4 of Mathematics for Machine Learning: Linear Algebra. The content of the course is excellent and professor David Dye's lectures are to be commended no doubt. The reason for my low rating is because the programming assignments are broken and that's really not acceptable for paid offering such as this. To clarify, at various points throughout this course, students are asked to complete a programming assignment. The student is presented with a button which says, "Open Notebook". The student is supposed to click this button and be redirected to a Jupyter Notebook (and interactive Python execution environment). Unfortunately, instead of being redirected, click on this button results in a "404 Not Found" error. There are various discussions in the class discussion forum regarding this issue (some months old), but no action has been taken to resolve this issue. Luckily, someone taking the course managed to find the programming assignments and posted them on google docs for others to use. I've been working these which is fine, but as I said, we're paying for these courses, someone should be resolving this.
I never took Linear Algebra in university. The last time I got exposed to this topic was more than 10 years ago when I was still in junior high. This course is very condensed. Each video covers each topic relevant to ML very briefly and the instructors go very fast on explaining each topic. This means students have to do a lot more research on their own to really comprehend the concepts. What's nice about this course is the programming assignments. They give you a chance to apply math concepts to the computational model. Something like this you wouldn't have a chance to do if you don't spend on an online course like this one, I guess. Overall, I think this course provides values in a way that gives you an overview of how Linear Algebra is used in ML. For me personally, I know I still need to consult other sources online to further understand Linear Algebra as I'm not sure that after finishing this course I've got adequate knowledge to pursue ML. What all that said, hence I give this course 3 stars.
автор: Avinaash S•
The lecture material in this course is great, and the quizzes are a lot of fun and it provides good resources for learning. However, the programming assignments are a pain due to lack of guidance and the grades are penalized due to minor things like indentations as opposed to actual math errors. This isn't a python course, its a math course, and grades should be awarded and penalized based on the math skills one has acquired throughout the course, not on the programming or whether an indentation is off. I highly recommend the course to learn linear algebra but I strongly encourage the instructors to improve the programming assignments or alter the assessment methods.
автор: MR T•
It must be difficult to pitch the level of these courses.
I have been taught Data Science whilst on an apprenticeship but didn't feel the maths was taught rigorously enough and hoped this would fill gaps of in knowledge.
The breadth of the concepts covered on the course achieved that but a lot of research was required from other resources to clarify certain topics which is why I think a beginner rating for this course might not be fair.
If you are not confident with maths, this course is achievable but expect to devote time to on other sites.
The PDF supplement is concise but useful for reference
автор: Meng Y•
Sometime the course does not clarify some principle. Also, I still cannot understanding that why the eigenvectors have relationships with page rank and why can we use the probability of reaching the link to each page as a vector. I cannot understand the relationship. Plus, the final quiz contains something that I have not learnt in the course, such as damping. I still cannot understand the Quiz2-5. I learn much in courses week 1-4, but I am much confused about the week 5. Thank you for listening.