Was a 3 stars until final week. Stuff is explained and is written poorly. I honestly felt like I got shammed by last week. I had to look online at other YouTube videos and forums and I am just not happy with what I got out of this course. I will be doing Andrew NG's course on YouTube now..

This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.

one of the best shot term course in Mechine Learning

Could have explained a little coding in videos instead of putting it in labs...

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step.

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

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

Thank You Mr. Saeed Aghabozorgi for designing and delivering such a immersive course, I found lot of pointers and specific details associated with many interesting topics in Machine Learning.

I liked it very much and was able to clearly understand the usage in programming language with ML related libraries. Thanks to IBM friends and Coursera for providing the expertise and the platform.

Very interesting subject, and very well explained. Even if I miss more concrete code examples, I can always look for it, the theory and the logic behind it was explained flawlessly.

The course is amazing. It provides with Mathematical equations for all the algorithms taught and coding is done with real world cases as well.

I liked this course. Nice to see how math learnt in theory years ago can be used in practice in some of the models.

puts a lot of the previous courses all together. challenging, but doable.

WORLD BEST STUDY'S MATERIALS ARE AVAILABLE ON COURSERA.

The course was well described. Thanks Man !!

The course covered quite a wide range of topics in Machine Learning, which was great. However, the sample code was not commented as much as I would have liked, at least for visualizations of the results of the machine learning algorithms, so I don't feel very confident that I would be able to replicate many of those on my own. The in-lesson exercises mostly consisted of following examples arranged by the instructor, there weren't many opportunities to challenge yourself with exercises and get feedback.

labs are not easy to understand

Hi,

I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.

(*): https://www.coursera.org/professional-certificates/ibm-data-science?utm_source=gg&utm_medium=sem&campaignid=1876641588&utm_content=10-IBM-Data-Science-US&adgroupid=70740725700&device=c&keyword=ibm%20data%20science%20professional%20certificate%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=347453133242&hide_mobile_promo&gclid=Cj0KCQjw0Mb3BRCaARIsAPSNGpWPrZDik6-Ne30To7vg20jGReHOKi4AbvstRfSbFxqA-6ZMrPn1gDAaAiMGEALw_wcB

Using IBM Watson Studio 'Lite' plan is a huge pain in the ___. I had to use 4 different emails to start from scratch to submit the notebooks for peer review. The course's instructions don't mimic the actual site - sometimes I wonder if they're referencing the same site in the instructions. You can learn this information elsewhere without added the headache.

Too much maths. Those who didnt have any background in math, it is very difficult for them to pass this course. They wont explain the code. They explain only the concept. The code is very difficult to understand. Very complex code. I many times thought of giving up. Finally completed with luck and hardwork.

That course is a joke.

Videos are less informative than wikipédia, hands-on labs have praticaly no exercises and are really shallows.

Finally the Peer-Graded Assignment is made even more difficult, because not having correct lessons and hands-on is not bad enough, by being really bad worded

Its interesting given the title the lectures never mention Python or show code ; ) That's left to the ungraded exercises. I liked it this way. Getting the good background on the algorithms independent of language or library, and then applying that in the labs is effective. I will refer back to this class as I continue learning about ML.

I had trouble getting my final project graded - but realized I hadn't shared my project correctly (at first didn't share code cells), and had to save a `version` of the notebook so my edits would be available to the other students to be graded. Leave yourself extra time for your peers to review your project, and check that the shared link to your notebook shows what you expect. You don't need to post in the forum to get your project graded - lots of students were doing that..

This course took about a month longer than needed and this was due to factors out of my control. My IBM Cloud subscription reached its monthly allotted data allowance and IBM wanted $200 USD to get more space. So I am paying Coursera a monthly fee and then I am being asked for more money from IBM to have the ability to do what Coursera was asking me to do in the course.

I reached out to the instructor group about this issue and I was told to use the Skills Network Lab. This also cost me time because the Python that worked in IBM Cloud's Data Park did not cleanly line up with the Python version in the Skills Network Lab. This caused me to go back and rewrite portions of my final assignment. Again, costing me time for things that are not in my control.

My monthly fee to Coursera should be enough for me to do what is being asked of me in the course and it is ridiculous that this is not how Coursera views the situation. My ability to do the work at my convivence was a major factor in me deciding to do the IBM Data Science certificate course. Obviously this is a false narrative.

In hindsight, I wish I never signed up for this certificate program. It has felt like a giant commercial for IBM products and then I actually got to the point to where they were overtly trying to milk more money out of me.

Course is largely a scam. At the end you have to have a peer reviewed project that will prevent you from finishing the course if other people do not grade your project. You can have a high enough overall grade that you could get a 0 on the final and still pass and still be out of luck if people decide to not help with grading, which is exactly what happened to me. Do not waste your time and money if you want to be at the mercy of other people.

1. Improve their work: Constructive feedback from peers or instructors guides learners to refine their skills and understanding. 2. Learn from others: By reviewing peers' work, learners can gain new insights, perspectives, and approaches to problem-solving. 3. Develop critical thinking: Evaluating others' work enhances critical thinking skills, as learners analyze and provide feedback. 4. Earn grades: In some courses, peer reviews contribute to the learner's overall grade. 5. Enhance learning experience: Reviews foster engagement, community building 1. Peer review: Learners evaluate and provide feedback on each other's work. 2. Instructor review: Instructors provide feedback and guidance on assignments. 3. Self-review: Learners reflect on their own work, identifying strengths and areas for improvement