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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.

4 stars only. The course was good. No problem with that. However, IBM keeps to update GUI of their cloud. Instructions provided in this course are obsolete.

Another thing, for someone, who didn’t take machine learning courses somewhere else, the amount of theory presented here is not enough. It is fine that you can put stuff inside your python (yes, I said this), but you have to understand, why are you doing this. You have to be understand how does it work. These libraries is just to try something out fast. The real implementation of the sophisticated algorithms is much more complicated. That doesn’t mean you have to be a PhD to do it, but you have to understand basic math that is going behind the curtain. It’s enough even for one algorithm. How many will know the difference between bias and variance after this course? How many will be able to say, how it can be fixed? Try to answer on this question.

Regards,

1. The presentation could be improved through subtitle and some funny way of presentation in between so that the long lectures are more interesting.

2. Multiple class classification and similar text based subject must be put throuhg a video presentation instead of text documents. Text dcoments are available in other domain aswell.

3. Lab sessions could be easily faked by a learner. There must be some grading to the lab sessions as well, you may include a small test in side each of the lab session and based on the user's attempt to solve the problem, the grades could be assigned.

4. There must be a summary video/reel or recape video (30 Sec to 1 Min) before starting a fresh video session.

It seems to me that it covers a lot of ground but lacks in depth. Also, the labs definitely should be harder to guarantee real understanding of the material, in my opinion. After finishing the course, I feel that I have been exposed to good quality material on ML, but I don't feel I have really put it into practice, although I have some code to reference if I need to. The peer review assignment was clear (I had seen a lot of complaints in the reviews, maybe they have fixed it) (this one could also be more challenging). Overall, I think it is still worthwhile as an overview of ML algorithms, applcations and related python libraries.

I was thoroughly impressed with the high quality of the videos and course material. They were exceptionally clear and easy to comprehend, making the learning experience enjoyable. However, I feel that the final project fell short in adequately assessing our skills in the Scikit-learn library. It lacked sufficient emphasis on data visualization, which is crucial for a comprehensive understanding of the subject. As a result, I am uncertain if this project would be deemed worthy of inclusion in one's portfolio. I hope that future iterations of the course could address this aspect to provide a more well-rounded learning opportunity.

The content of the course is very well designed and it is very easy to follow. The Teachers have done a fantastic job explaining the content.

I would like to make the following suggestions:

There should be more hands on graded exercises. Instead of one exercise at the end, it would have helped if relevant section was graded after it was taught.

There is too much focus on Classification. Machine learning consists of equal parts of value prediction and class prediction. There is nothing on things like Linear regression. It should have at least been included as a ungraded exercise, so that I know what Python functions are used.

One of the best course for the beginners who want to learn the machine learning concept from basics along with the theory. The course lecture only contains the theoretical part but the lab part are only being instructed within a notebook link. This course is great but can be improved by adding some lectures of the lab or practical part by specifying how those codes are being implemented. Although the Notebook also explains the best and also helps in learning the practical skill. The assignment given helps a lot in learning the modals easily and visualizing the result.

The content is impressive as it offers practical applications of machine learning and presents the mathematical concepts in an easy-to-understand manner. However, some improvements are needed regarding the presentation of the python part. Currently, there are no instructional videos available for the use of libraries and methods, and the laboratory instructions only offer written guidance. This may be challenging for those who lack prior knowledge of Python and machine learning libraries, and a more interactive approach to the teaching material could be beneficial.

A very good course to learn the basics of ML. Several in-depth topics are not covered stating that they are out of scope for this course.

The course allows us to use an online tool for lab work and assignments with many relevant libraries, thereby avoiding any software/library installation issues, etc.

There are relatively less number of videos but they are to the point.

Labworks need to be self-learnt(no separate videos for code), although the notebooks that are shared tries explaining the code a bit.

Overall for a new learner in this field, it's a good start.