Chevron Left
Вернуться к Mathematics for Machine Learning: PCA

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

4.0
звезд
Оценки: 2,340
Рецензии: 587

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

JS
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.

NS
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:

76–100 из 582 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Nuria C

3 нояб. 2020 г.

I did the other two courses of the specialization, which I found great. They clearly explain concepts and give examples. In this course, the professor basically writes down definitions as you can find in any maths book, with no explanation and barely no examples. So, I found myself lost on the quiz and programming assignments. I am quitting the course even if I paid for it, since I feel is it not being a good use of my time. It is true that it is indicated as intermediate level, while the other two courses were for beginners, so I guess I am just in a course which is not for my level. I just don't know then why they included all three in the same package? :/

автор: Aniket D B

2 окт. 2020 г.

Do not take this course. This course is just a waste of time, money, and effort. The instructions in this course are vague and useless. You have to learn everything from the internet in order to answer the quiz. The programming assignments are so poorly designed that there is no difference between a blank notebook and programming assignments in this course. The grader will grade everything wrong even when your code is correct. You have to do extra maneuvers in order to get your assignment graded correctly. IF I HAD AN OPTION OF GIVING A NEGATIVE RATING I WOULD HAVE GIVEN THIS COURSE A MAXIMUM NEGATIVE RATING. EVEN 1 STAR RATING IS TOO MUCH FOR THIS COURSE.

автор: Shubhayu D

13 июня 2020 г.

The first two courses in the specialization were extremely good. However, this course is nowhere close to them. Neither does the instructor provide enough intuition, nor do the assignments help in the learning process.

автор: Abhishek S

7 июня 2020 г.

The first two courses of this specialisation were awesome PCA being a hard topic is difficult to understand but the course was boring and not good compared to previous two.

автор: 용석 권

29 янв. 2019 г.

Programming assignments' quality is too bad to follow it. Their lecture's explanation and assignments' notation are not matched. Moreover, the code is sometimes ridiculous.

автор: Benjamin F

18 нояб. 2019 г.

The didactic value of this course is rather low. The lectures do not explain the very concepts required to sovle the subsequent assigments, or do it in a very poor way.

автор: Kareem T M

18 мая 2020 г.

Worst Course I have ever token on Coursera, the instructor hadn't mention any examples or simplify the information.

автор: HARSHIT J

11 июня 2020 г.

Very tough course, the first 3 weeks are good, but the last week is as poorly explained as one can imagine

автор: Michael-John B

16 мая 2020 г.

If I could give it negative stars I would.

автор: Mohamed S

1 июня 2020 г.

topics are poorly explained and confusing

автор: Anton K

14 нояб. 2020 г.

By far, this is the best out of 3 courses in this specialization. It is hard though and in the weeks 3 and 4 I had to pause and rewind almost every 10 seconds of the videos and search some error in code labs on the web. But in the end this course showed me in great detail the process of PCA and I also learned a bit of linear algebra alongside it. Considering problems with this course, there were some points that got me a little bit dissapointed. I still don't get it why are we using the biased version of variance, sometimes the notation changed a little bit, (which is not a big problem but introduces some inconvience if the material is completely new to the learner), some of the math concepts were not covered in the "linear algebra" course. But the worst problem was a technical one: some parts of the labs that are not necessary for grading but are very important for learning were throwing errors. I hope that in the future versions they will be resolved.

автор: Marco v Z

19 июля 2020 г.

I was somewhat put off by critical comments about the third course in this series, but have to disagree with the reviewers. Yes, it is tougher and, yes, the instructor doesn't have the "schwung" of the other two instructors, but that doesn't affect the quality of this course. His walkthrough of the derivation of PCA is thorough and systematic, and builds on material that has been presented in the earlier lectures.

In fact, looking back on the entire specialisation, I would retrospectively grade the first two courses a notch lower (even if they're excellent), simply because they "sailed through" a bit too easily. The exercises in those courses required little thinking apart from recalling what was said in the lectures. In this course, exercises tended to go beyond or ahead of the material presented in the lectures. Solving them required active thinking, reading, and problem solving, which in the end brings a more thorough understanding.

автор: Heinz D

21 нояб. 2020 г.

Good and motivating lecturer with decent language, thank you! Challenging course but the relief at the end is great. I'd prefer if the lecturer wouldn't write his texts to the very border of the board because if I'm taking screenshots in PiP mode, the window's controls (close window, play, return to normal video mode) are overlapping.

Week 1: Pre-course survey contains the questions of rather a post-course survey. The lab / programming assignment contains misleading code segments and incomplete explanations.

Week 2: Quiz 'General inner products', dealing with 3-dimensional inner products is very challenging as the lecture only went - in an extreme hurry - through 2-dimensional examples.

Week 3: Programming Assignment contains misleading code segments / comments (e.g. contradiction concerning return variable in project_1d()).

Week 4: Video 'Problem setting and PCA objective' -> Download Link to the PCA book chapter goes to Nirvana.

автор: Fredrick A

20 февр. 2020 г.

The coverage of PCA provided by the instructor was wide and provided me with an intuitive basis for executing the PCA algorithm in the wild. Ultimately, the subject and its various steps were easy to understand. That said, I gained many great insights watching Khan Academy videos especially ones on eigenvalues/eigenvectors. By far the hardest part of the class was implementing and executing the python code. There the devil was in, and sometimes, outside of the details. I cursed the name of the Instructor more than once (a lot more). But, in the end, because of the real life, no safety net experience, I was able to jump right into PCA (and other feature engineering projects) adding value to my team at work on day 1.

автор: Abdu M

20 янв. 2019 г.

Best course out of the series so far. A fine balance between theory and derivations, and practice with the programming assignments. It seems that they have solved their programming assignment issues (the first one still has some problems with the grader I believe). This course does require you to have some prior experience, though, so if you are new to programming or linear algebra (not just the concepts but how to apply them) it's bets to take the first two courses with some additional help (maybe Khan academy or even MIT OCW. I will certainly refer to this course in the future, as well as the professor's book on Mathematics for ML.

автор: Anamitra S

4 сент. 2020 г.

Even though one might read quite a few negative reviews about this course, I having completed this course certainly can tell that I learnt the most while doing this course. The course was indeed hard and challenging but the good thing that came out of this course was it gave me the ability to learn to study quite a few topics extensively on my own. The course had the book on "Mathematics for Machine Learning" which acted as a great supplement to this course. Overall, I'd ask anyone who is seriously interested in learning the extensive Math behind Machine learning, to take this course.

автор: Laszlo C

6 дек. 2019 г.

This is an excellent course first covers statistics, looks back to inner products and projections, thereafter it connects all of that and introduces PCA. The knowledge that you've gathered throughout the first two courses gets applied here. Granted, it's more abstract and challenging than the others, I wouldn't give a worse rating just because of that. You'll need to dive into certain topics on your own and if you strengthen your coding skills for the programming exercises. Nevertheless, it's just as highly rewarding as the first two.

автор: Mohit J

28 дек. 2020 г.

This course is too good ,difficult level of this course from other too of this specialization is more.

Having patience and more practice lead to more successful .

If anyone want to learn Machine learning course then after doing this Machine Learning course is simple because most of the thing you have learn through this course

This specialization makes you better and better and you learn many more new and interesting thing related to real world example with practice assignment

Thanks a lot for this to all the mentors

автор: Douglas W

22 мая 2020 г.

This was the most challenging of the three classes in the series. I thought the instructor did an excellent job of moving from theory to practice, and in the end I came away with a good understanding of the topic. As a developer, part of my personal learning style is to shadow these types of lectures in code. I did (or attempted) naive implementations on most slides - that definitely helped my comprehension of this challenging material. Be prepared to work hard, occasionally head scratch and you'll do fine.

автор: Muhammad Y A

5 нояб. 2020 г.

Big thanks to the teacher, this is the most challenging course among the other courses on this specialization. It took me a full 24 hours to complete the final assignment, PCA Algorithm. But, it's worth it, I really enjoyed this course besides how hard it is lmao. One more, unfortunately, there will not be much discussion on the forums, since there's a few people enroll in this course compared to others and the assignment, especially the last one was very hard, anyway hope you enjoy this course, see ya.

автор: Jitesh J T

23 дек. 2019 г.

Hi,

The course tries to cover most of the important mathematical concepts in Mathematics applied to PCA. The assignments were a bit tough, but i guess that the road ahead when we do programming for data sets in real world applications would not be that easy. Loved the way the lectures were delivered and the programming assignments help to build a strong base for applications of linear algebra that we have done earlier.

Thanks and Regards

Jitesh Tripathi, PhD in Applied Mathematics

автор: Taranpreet s

26 сент. 2020 г.

Although there are glitches with the submission of assignments and some of the videos by instructor are brief, I will still rate it as a 5 star for the content covered. This is 3rd course of the specialization and need solid understanding of the concepts covered in first 2. Considering the more challenging content covered in this course, Instructor did a great job. All instructors in the specialization are awesome, Would love to do more advanced courses from the same team.

автор: Tarek L

11 сент. 2019 г.

This is a difficult course, but it really gave me an appreciation of the mathematics behind machine learning. I encourage anyone doing this course to read Deisenroth's free book Mathematics for Machine Learning (mml-book.com) to better understand the notation and technique used to get to the proofs. If anything, the rigor of this course inspired me to further pursue learning in mathematics to strengthen my machine learning foundation.

автор: ChristopherKing

18 апр. 2018 г.

The whole content of this course is fantastic, not all details were covered in the video, but main ideas were expressed in a great way buy math formulations. Pay attention to those vectors and matrices, especially their dimensions, this will help you solve problem quickly. More important, matrix is just a way to express a bunch of similar things, knowing the meaning of those basis vectors is important.

автор: Sriram R

18 июня 2019 г.

This is one of toughest course in this specialization. Having said that, it was interesting to learn about the inner working of the PCA and is well taught. At times it was tough to follow and could have been better if there are some additional examples explained to reinforce the concept. Also week 4 is kind of rushed with little or no time to fully appreciate the beauty of PCA.