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.
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.
автор: Ashok B B•
Course was challenging , but learned the maths behind PCA,
автор: Cesar A P C J•
Good content, just need to fix the assignments' platform.
автор: Dave D•
This course was a fair overview of a very complex topic.
автор: ADITYA K•
It is very informative and hands-on based Course for PCA
автор: Md. S B S•
Not as good as the other two courses..but interesting!
автор: Sharon P•
Mathematically challenging, but satisfying in the end.
автор: Paulo Y C•
great material but explanation are a little bit messy
Good course, but requires mathematical background
автор: taeha k•
Good but slightly less deeper than the other two
автор: Eddery L•
The instructor is great. HW setup sucks though.
автор: Manish C•
Best course for machine learning enthusiast
автор: Thijs S•
The last assignment could use improvement.
автор: J N B P•
Good for intermediates in linear algebra.
автор: Romesh M P•
Too much non-video lectures (lot to read)
автор: Tanmoy S•
The last course could have been better.
автор: Kailash Y•
Challenging but in a good way.
автор: Muhammad F T S•
this was hard but insightful
автор: Mark R•
Good, short, overview of PCA
автор: Changxin W•
Many errors of homework
автор: Poomphob S•
so challenging for me
автор: Sammy R•
Needs more details
автор: Shreyas S S•
автор: NITESH J•
автор: Raihan N J M•
автор: Harrison B•
Broadly speaking, this is a good course. However, the feeling is that it should be twice as long and with more videos. There is simply not enough instruction to facilitate clear learning and completion of this course is down to an individual's desire to read around and problem solve.
In particular, the programming assignments - whilst not technically difficult, lack clear articulation of expectation, which is compounded by pythons slightly inconvenient handling of matrices. Writing vectorised code which involves 1 x N or N x 1 matrices and transpositions often results in zero marks; with no clue whether the code is wrong, the student has misunderstood the expectation or python is refusing to recognise a N x 1 matrix. This could br helped by including more discriptions about the data sets and the variables being used, as well as the expectation of the output.
There are a lot of positives about this course, the videos are well made and are clear. Excellent supplementary learning if you're doing undergraduate Linear Algebra or other Machine Learning courses; just a bit too cramped for a standalone course (even with the others in the specialisation being well understood). Perhaps a four course could be added to this specialisation for "The Basics of Python for Machine Learning" where a student covers all the relevant coding knowledge?