WS
Jul 6, 2021
Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.
JS
Jul 16, 2018
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.
By Sharon P
•Sep 24, 2018
Mathematically challenging, but satisfying in the end.
By Paulo Y C
•Feb 11, 2019
great material but explanation are a little bit messy
By Anas E j
•Jun 19, 2022
Thank you for this course , hope to learn more !
By Wd E
•Feb 21, 2021
Good course, but requires mathematical background
By taeha k
•Jul 27, 2019
Good but slightly less deeper than the other two
By Eddery L
•May 24, 2019
The instructor is great. HW setup sucks though.
By Muhammad B A
•Mar 26, 2023
its was soo hard your background not from math
By manish c
•May 6, 2020
Best course for machine learning enthusiast
By Thijs S
•Sep 28, 2020
The last assignment could use improvement.
By andre w
•Mar 27, 2022
a really good course but also really hard
By J N B P
•Sep 10, 2020
Good for intermediates in linear algebra.
By Romesh M P
•Jan 16, 2020
Too much non-video lectures (lot to read)
By Apriandi R A
•Mar 26, 2023
Overall very fun and make a little dizzy
By 3047 T
•Jul 13, 2020
The last course could have been better.
By no O
•Jul 9, 2020
Challenging but in a good way.
By Muhammad F T S
•Mar 28, 2021
this was hard but insightful
By Deleted A
•Jan 22, 2019
Good, short, overview of PCA
By Changson O
•Jan 28, 2019
Many errors of homework
By Poomphob S
•Jun 18, 2020
so challenging for me
By Sammy R
•Dec 25, 2019
Needs more details
By Shreyas S S
•Jun 20, 2020
Good Course
By NITESH J
•Aug 28, 2020
kinda long
By Egi R T
•Jul 14, 2022
Good
By Raihan N J M
•Mar 12, 2021
okk
By Harrison B
•Apr 18, 2020
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?