Chevron Left
Вернуться к Mathematics for Machine Learning: PCA

Отзывы учащихся о курсе Mathematics for Machine Learning: PCA от партнера Имперский колледж Лондона

Оценки: 2,582
Рецензии: 643

О курсе

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Лучшие рецензии

16 июля 2018 г.

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

18 июня 2020 г.

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

Фильтр по:

526–550 из 638 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Nicholas T

31 авг. 2020 г.

I found this course to be rather lacking in what it lists as pre-requisites. I found the need to take a course on numpy while I took this course. Also, I'm just confused as to why this is part 3 of the specialization. Why not do a section on probability/stats to prepare for machine learning? I like all the professors, but there's only so much you're going to learn. I found I needed to constantly use the resources, and they are good, but the resources were better than the assignments and instruction, so... I would suggest saving your money.

автор: noel s

22 июля 2020 г.

The intermediate level of this course is accurate, but mainly because of the course's structure. In my opinion this course should not be a part of the specialization as the PCA is already covered in the first two courses. Although this third class is more (and almost only) about the maths I found it confusing in relation with the previous course and their explanation of PCA. Programming assignments are difficult and help the student to think by itself, however they are buggy which may take away the struggling student motivation.

автор: Brian G

12 мар. 2021 г.

The weakest of the series of 3 in the Mathematics for Machine Learning Specialization. The course videos did not explain the material well enough and referenced significant amount of external reading sources. The videos are full of jargons without taking the time to properly explain them or help the learner develop intuitions. I walked away with a very muddled understanding of PCA even though I was able to complete all quiz and exercises. I recommend a revision to this course so this important topic can be taught better.

автор: Sagar L

21 мар. 2020 г.

Although the topics and lecturer's delivery were nice, but as compared to the two previous courses of the specialization, this one doesn't fare well. The content in the video lessons and that in the notebook were not really planned well in terms of scope. A participant who isn't already familiar with these concepts, would struggle a lot. Only if the reading material, video content and notebook assignments were designed keeping that in mind, it would have been better. Apart from that it was a good course.

автор: Vitali Z

22 авг. 2020 г.

Slow notebooks, bad explanations, unclear what to do in the notebooks.

I don't know why i spent so much time to finish the course- maybe because of my perfectionism didn't let me stop trying.

I guess the matter itself is good, but:

1. you probably got to re-record all the videos a little more bit by bit with more examples

2. fix the slow notebooks

3. more assertions for each function instead of for the whole thing in the notebooks

4. more detailed explanations what we are even doing there

автор: Tobias T

14 июля 2019 г.

If you like traditional lectures, which you go into a math class then feel puzzled, then go for it. Otherwise, the contents of this course are simply going through the mathematics equations and definitions, which can easily be found in textbooks. Ironically, the previous two courses in this specialization used lots of graphics and animations to help you understand the maths (either in terms of equation-wise or intuitively), this course completely lacks this element.

автор: Mark C

30 июля 2018 г.

Only on week 1 but this is already a disappointment compared to the first two classes in the Math for ML series which were excellent. Some content is presented too fast. Quiz questions are ambiguous. I already paid for the class so I will finish the content but not worry about passing quizzes and assignments. Had I known it would be like this I wouldn't have paid for it. Check out the other reviews and forum discussions to see what others think.

автор: Max B

14 авг. 2019 г.

Pretty bad all around.

The teacher keeps throwing formulas without taking the time to explain why they are useful, and what they represent.

The first two courses were really good, and this one is a bummer.

Most of what I learned was learned elsewhere, the course acted as a detailed syllabus with some practice quiz (of relatively poor quality).

It's still worth taking if you completed the first two courses and want the specialization certification.

автор: Nouran G

11 окт. 2018 г.

Course is inconsiderate to new learners in that new concepts were very sloppily introduced. Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best. Unlike the other two courses, it assumes python knowledge, doesn't explain relevant syntax in the assignments; which made me take a lot of long unnecessary detours to get the python implementation right.

автор: Marvin P

24 апр. 2018 г.

After the other two awesome courses of the specialization this one stays far behind my expectations. Weakest course of the specialization. Instructor is obviously knowledgeable but does not provide much intuition. Programming assignments are really difficult and at many points frustrating. 2 more weeks and therefore comprehensive instructions would be desirable. Couldn't appreciate that course as much as I wanted to.

автор: Michael D

22 июля 2019 г.

After having done the first two parts of the specialization, I am afraid this one didn't stand up to the high quality bar the previous two had set. The programming assignments are unnecessarily long and complex and the overall material is not as engaging, connected and concise. I might give it a good rating as a standalone but now I can't avoid comparing it to the other two parts of the specialization.

автор: Ricardo F

4 мар. 2021 г.

This course is a great departure from the premise of bringing Mathematics and ML together. The instructor basically throws dozens of calculations, Math for the sake of Math. No intuition is gained, either. The instructor is only preoccupied with writing down the calculations. You either "swallow" all the Algebra or not. I'd suggest the complete reformulation of the videos.

автор: Daniel A

9 мая 2020 г.

Compared to the first modules in this series, the instructor explains almost none of the intuitions behind the maths and will skip over large essential pieces required to complete assignments and quizzes. It assumes a wide knowledge of programming and broader maths that was handled significantly better in the earlier courses.

автор: NamTPSE150004

11 февр. 2021 г.

The explanation of the course is hard to understand. Some misunderstanding. I have to study on youtube or somewhere to pass this course. The videos in the course are lack information. 2 stars because of the PCA assignment helping 1 plus star for the course. The two courses previous in this specialization are good.

автор: Xiaoxiao L

4 янв. 2021 г.

This is the least effective course among the three courses in this specialization. The reading materials have no context. People who have not been around those math symbols have no idea what the reading materials mean. They are not intuitive at all. The design of the assignments are poor as well.

автор: Alois H

18 февр. 2021 г.

This course has been a nightmare. Dense and obscure lectures, "challenging" assignments asking for things that haven't been properly taught in the lectures and often unclear instructions.

Yes, some useful concepts are taught but overall it's rather a waste of time.

автор: Daniel U

27 сент. 2018 г.

Programming assignments seemed to be written from a completely different direction, and instructions are vague and misleading. (The math assignments were not so bad.) There was no staff or mrntor engagement in the forums during the period of the course.

автор: amit s

8 февр. 2019 г.

Unlike the prior courses in the series, topics not clearly explained and brought too sudden. Also none of calculations shown completely, instructor just wrote results in the end. Due to all these reason I was not able to finish the course.

автор: Kevin L

11 сент. 2018 г.

The course assignments could be improved dramatically, though the course itself has very good content if you want to have a taste of how linear algebra (predominantly) can be implemented to solve machine learning problems.

автор: shashank s

17 февр. 2020 г.

First two courses in this series are great but not this one. Lectures and exercises are not related. I do not feel like I have totally understood PCA. Was able to complete the final assignment thanks to the internet.

автор: Bohdan K V

13 авг. 2020 г.

The course is awful, it's nothing compare to previous 2 courses. It has a lot of errors in assignments objectives and quizzes! The explanation is complete crap! I'm wondering how was it even allowed on Coursera?!

автор: Ivo R

16 нояб. 2019 г.

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

автор: raghu c b

4 апр. 2020 г.

Needs to demo a little bit of code owing to the complexity of the course content.Lectures gives just a high level understanding only. Assignments are taking far more complicated than expected.

автор: Paulo H S G

27 нояб. 2020 г.

Even though the videos and quizzes are well produced and informative, the assignments are so poorly designed that they can only bring about some frustration with the learning process.

автор: Anjali s

23 авг. 2020 г.

Faced a lot of problems in exercises. Don't feel that i have completely understood the concepts. This course can be made more learner friendly with better explanations.