The course touches all the topics that are of interest for the a Machine Learning pratictioneer. I've found the course sometimes oversimplified, that paradoxically made it harder to grasp some concepts, expecially the topics of the Week 2. Overall I've found It to be a good course because at least it gives you the path to follow from where you can study on your own to go deeper in the topics you are interested.

Note: I would suggest to edit the notebooks. It is not a good idea to have the solutions in the same notebook where you should do an exericise, because it makes also the video lectures that came after pretty useless. I suggest to separate the exercises from the solutions, and to put the solutions in the video lectures so you must follow them with some focus to understand what the solution was. Furhtermore i would review the notebooks. Some of them were different from the ones presented in the video lectures which made it a little bit confusing to follow.

The Course is quite detailed and well explained regarding the techniques and fundamentals required for exploratory data analysis. Sometimes although I found the contents being spoken in the video hard to understand because of the flow and the accent, but then reading the subtitles helped. Also, one suggestion would be to provide a presentation or some pdf documents for the most commonly used Python commands for various libraries like Pandas etc. for data handling (starting from data reading, cleaning upto hypothesis testing and further). This is because to makes hand notes of all the commands from the demo videos takes quite some time.

All in all, big credits to the team for such a well prepared course material!

The course was really helpful in understanding basic ML concepts and the computational framework we can use for EDA.

But a lot of students had problems with ghost reviews where they received 0 points across the rubric. It took me two days to finally get my assignment graded properly and lost significant time in correcting the problem. Coursera should really do something about this issue.

The speaker appeared to be a third party - reading off a script, and not the actual course instructor. This aspect made the subject drier that it actually is.

As for the course and the notebooks, it has been done very well.

I would still rate this as a 101 in terms of real depth of experience, but perhaps that is as is expected.

The assignments do not do a regirous test of skills aquired.

The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.

It doesn't cover T-tests and f-Tests which are often used. Also could be better descriptive about some topics.

Poor explanation of many concepts. Felt i the instructor was reading the material in a neutral manner and was not emphasizing on key moments. Also lack hands on opportunities and practice to help understand the concepts.

Overall seemed more like a summary of various titles and definitions.

instructor does not capable to develop the user interest in course. Just reading the slides

difficult for a beginner.

The instructor was not actually teaching, but just reading from the text. His lack of mastery of the topics was quite evident, especially in hypothesis testing, which was extremely confusing. I advise those who want to take this course to only study the notebooks and search the internet wherever necessary because the videos will not add anything special to you.

i purchased whole machine course but after payment i can only able to access 1 course among all 6 and they ask me to pay extra for another 5 course.

not worth. just a guy speaking . no practical knowledge by teacher . how can i learn everything in the lab

Very poor course structure

Very vague explanations.

bored

Although I had done such data analysis elsewhere in Coursera, this I found very comprehensive and systematic. I wish the topic of statistical significance tests was covered in some detail based on real data, rather random data generated for the purpose. I feel this area should receive more attention from the designers of the course. Thanks for all efforts put in by the faculty and all support person in the background. Thanks a lot..

As someone with a science background, exploring and visualising data as well as performing hypothesis testing is something I have already done a lot of. This course offered a very useful refresher in these topics, as well as introducing me to a lot of tools that can streamline my work in these areas. The course was very well presented and the coded examples are useful to keep as go-bys for use in future work.

I am on the IBM machine learning specialization professional certificate track and this course is my first course in the track. It is a very simple course, but it touches on the most important topics before performing any machine learing related work. I highly recommend to complete the machine learning specialization certificate after completing the course.

This course is an engaging and informative journey, keeping motivation high throughout! Even if you don't get it right away, don't give up — work through the notebooks, and everything will start to make sense. Completing it was a huge confidence boost, and I'm thrilled with the progress I made. Highly recommend for anyone looking to grow their skills!

Very friendly and extraordinary course for those who are looking for machine learning profession. The Data analysis and other process were well taken throughout the course. The Teaching members are well qualified and understandable so that we can have a clear thought on a particular concept. Finally an awesome course that no one should miss!!!

This is the first course where you will learn how to use Jupiter Notebooks. For this purpose, you will learn machine learning concepts and more. It is not designed for beginners to learn. The prerequisite is that you should have some knowledge of mathematics, as some mathematical formulas such as linear algebra will be used in the course.

Thanks Coursera

my life has changed after Corona crisis and founding you!!!

Recommended for beginners as well as for those students, professionals who want to get their hands dirty in the data science life cycle.

Thanks to learning on Coursera , I'm able to add my courses to my Linkedin and resume that make me stand out from my peers.

I would highly recommend the IBM Machine Learning course to anyone interested in data science and machine learning. The course was well-structured and easy to follow, with plenty of practical examples and hands-on exercises. I appreciated the opportunity to apply what I learned through real-world scenarios and projects.

Peer Review needs some moderation, someone marked all zeros, for one of my assignments. We are doing Machine Learning clearly an algorithm for such can be made available. Overall a great Introduction and hands-on guidance towards the Tools and Statistics involved for various business applications in the real world.

This course helped me to understand basics of AI /ML, Data Analysis and Hypotheisis Testing. Indepth explanation of some topics were plus point of the couse. Now, I am capable of doing Data Analysis with 100% confidence.

Thank you @Joseph Santarcangelo, @Svitlana (Lana) Kramar (Instructors) and IBM