RN
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Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
JL
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I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
By Nguyễn H V
•Apr 3, 2020
Thanks
By LATCHIREDDY S V
•Mar 12, 2023
super
By Ayush K
•Sep 26, 2021
Great
By Cristian C P Á
•Nov 14, 2019
Good!
By kanimozhi g
•Sep 14, 2024
good
By Abdoulaye W D
•Oct 7, 2023
GOOD
By Muqseet F
•Apr 17, 2023
good
By Girija S M
•Aug 24, 2022
nice
By A R
•Feb 20, 2022
good
By Anshuman R
•Jul 15, 2021
good
By Mullangi T
•Jun 21, 2021
GOOD
By SHALINI S
•Sep 6, 2020
Good
By Zakir H
•Jul 19, 2020
Good
By Sudhanshu R
•Jun 12, 2020
good
By Tejas S
•Apr 28, 2020
good
By VIGNESHKUMAR R
•Dec 26, 2019
Good
By Lakshmi N
•Dec 10, 2019
Good
By lokesh s
•Jul 17, 2019
good
By Hiep D X
•Oct 18, 2022
ok
By syed s
•Aug 8, 2021
wow
By piyush s
•May 19, 2020
ok
By Pagadala G s
•May 18, 2020
Ok
By RABAB E
•Dec 14, 2023
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By Malte H
•Jan 11, 2021
PRO: Good overview and basic introduction of common machine learning techniques.
CON:
- The final assignment is peer reviewed! I saw no mention of this before purchasing the course. This means you are at the mercy of other students who may have less experience than you and may notbe qualified in grading assignments. Also it may mean you have to wait a long time before you get your certificate. It would be better to implement a Kaggle-style assessment of the models and use that to obtain a score and turn that into a grade. This would be transparent and instantaneous.
Some of the forum answers provided by the teaching staff are half baked and often inconsistent. e.g. they give example code for making a figure and and also a figure. But the figure is obviously not made with the provided code and the code contains typos. This is frustrating and makes learning harder than it should be.
Some of the code in the lab exercises don’t obey good practices. e.g. in every lab the data is normalised before train/test splitting. In the final project there is a comment that this should be done the other way around (and it really should!). Why not do it the right way in all the examples throughout the course?
By Isabel L
•Apr 9, 2021
The course provides a good overview of the topic over 5 weeks plus the project week. With previous knowledge of Python, the coding is easy to follow. The videos are good. However, the Python Jupyter notebooks provided could be significantly improved. The content could be of better quality and more rigorous. The notebooks have many spelling mistakes, few explanations, unnecessary imports, a few bits of code that are incorrect and need to be fixed, some unnecessary or incorrect statements, etc. Some of the exercises proposed in the notebooks are meaningless for learning. Better practice tasks could be thought. Different notebook parts are clearly written by different people with different coding styles, which can sometimes be confusing for the learner. The assessment (classifier of loan repayment data) could also be improved as it was confusing in terms of what data sets should be used for training and testing. Peer-review is perhaps not the best for assessment grading either. Overall I enjoyed it and learnt, it's a good first impression of the subject but I would have expected higher quality of the materials from IBM - Coursera. Also, it would be good if notes or slides were provided.