okk

na

no

ok

NA

ok

ht

<3

ff

m

.

D

d

,

.

.

U

h

A

Thanking Coursera for providing me the free education and helping for my substantial need

as I was not able to afford the course fee ; literally I can't express the happiness of

mine in words and how much I'm thankful to coursera cannot be described but heartfully am

feeling blessed by the coursera for helping me..Thank You Coursera with Love.

And none the less the instructor was very helpful throughout the course and along with the

discussion forum is also a great way to share and being helped during any problematic

situation but one thing I would like to add the lab tools are not available most of the time

but hopefully got to managed by practicing on my local Jupyter Notebook with the help of

sir's saeed aghabozorgi github repo. As I had some prior knowledge of Machine Learning so

the course was on intermediary level for me on scale of learning and enhancing my

introductory hands-on skills of training .

I have successfully completed the project although it was challenging but enjoyed a lot while

learning and building my final_capstone_project.

I've posted my project notebook very neatly and well maintained and have posted my notebook

with no hidden code cells to help others and inspire with my work.

If anyone wants to visit my github repo to final_capstone_project notebook feel free to commen

t down I'll share it with you happily :)

Thank You !

The content in this course is presented clearly through the videos provided, and the ungraded labs are quite helpful in learning how to implement the algorithms discussed in the videos. I took this course by itself (not as part of the IBM Data Science Certification), and there was some stuff I had to look up, especially about Pandas data frames and how to work with them. Maybe that content is covered in the courses before this one in the certification sequence. I didn't see a prerequisite knowledge list for this course, but that would be helpful for future learners who are considering taking this course by itself.

The reason I'm giving this course 4 stars instead of 5 is because of the typos that occurred, especially in the directions of the final graded project. I was able to read through the discussion threads about the final project to get a better understanding of what I was expected to do (because part of the directions don't make sense), but those posts are from over a year ago, meaning the typos haven't as of yet been corrected in the course.

Otherwise, I found this course to be enriching and enjoyable! Thank you!

The course is not for someone who is new to python. This course requires some prior proficiency and understanding of the language.

There are no professional notes at end of each module or section like some other courses, so you need to take your own notes while going through videos. Having proper summarized notes like the ones in Andrew NG machine learning course would have been great.

There has to be some proper videos / guidance notes or well documented pdfs focusing on the data pre-processing and related components in Python and all other details as well regarding training a model, assignments are directly provided to be completed in Python without any tutorial of the same

Overall a good course but it would be great to have all documentation. Also since the title itself makes it clear that course will be in Python, Kindly add videos to the course which help more understanding of all concepts through Python, currently all videos only have conceptual explanation but no video touches the Python component or how to go about the implementations in real world.

Thanks

This is a great course. I sort of wish that this course provides more detail on the statistics and data visualization to look at the models such as linear regression and decision tree, at the same time, I do understand why it is the way it is as this is more about the fundamentals of machine learning than learning more about statistics

My least favorite part of this course is the Honors Peer Graded Assignment. The peer graded assignment feels absolutely rushed. Hopefully the rubric and/or the assignment gets some changes in the future. It's not really big issue, but there are some discrepancies from what you've learned from the labs compared to the rubric on the PGA.

The videos are helpful, the ungraded labs are easy to follow. The quizzes are straight forward just like pretty much the rest of this certificate.

Overall, this is a great course like most courses in this certificate. Cannot give it lower than four stars. If the problem gets fixed in Week 6 with the Honors Peer Graded Assignment and the rubric, then I would give this a 5 star rating.

This course was very informative about the basics of machine learning, the standard ML models and how the underlying algorithms work, and ML process of importing, cleaning, manipulating, and ultimately analyzing data.

The Python aspect of the course is extremely high-level and honestly not that helpful. All the code is pre-written for you and often without full explanations for what its doing. Specifically, all the pre-processing, feature engineering, data visualization, and basic program-building is already done for you, so reproducing it in a real-world setting would be difficult for anyone without a computer science background.

Overall, this is a great course if you have previous programming/data analysis experience and are trying to simply familiarize yourself with the basics of popular machine learning models. If your goal is to learn how to build models from scratch for a practical application, you may want to supplement this course with others.