Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

I am not happy with the course material and the way teachers are teaching.

This is not a good course for beginners.

no teacher intraction

This course provides a brief introduction to many of the vast and dense ML concepts, like Regression, Classification, Clustering, Neural Networks and many more.I took a course by Prof. Andrew ng on Coursera before taking this course. And due to this reason, i was somewhat familiar with the concepts that are being taught in this video.If you are a beginner, i personally recommend you to take Prof. Ng's course on Machine Learning, and then switch to this part of specialisation, by completing the 1st specialisation (2nd is optional but if you are sort of artistic person, and have a habit of visualising things then opt this too). It is best for those who just want a quick recap of some topic.

The course is so informative and interseting.

This is an excellent course. If you will complete all exercises making sure you complete all questions in each exercise and score almost 100% in each quiz then you will get full value out of course. Deadlines can be reset any time so you can resume courses anytime and you can take your own time as per your schedule. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material.

Gives practical experience with ML in Python.

Hides the math under the hood :(

However, this course is not enough to become a real data scientist. One needs much more exercises.

A very comprehensive and hands-on course for learning applied Machine Learning. Many thanks for this course.

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

This is an incredibly dry course from the University of Michigan. In typical academic fashion, it churns out a bunch of lectures, expects you to remember the content, then throws you straight into some quite complicated problems. Half the time, these problems don't even work and you have to dive into the forums to find out how to correct mistakes that the content providers have failed to correct themselves, even several years down the line. There are iPython notebooks you can use to follow along with the lectures, but really they could do with useful information and explanation embedded within them, which is one of the main strengths of iPython notebooks and has been sorely underutilised here. If the course material were presented in a more interactive and engaging manner, the learner might be more motivated and engaged when solving assignment problems. As it is, unless you have prior knowledge or experience within the field, or a mountain load of free time, it's more an education in frustration than machine learning.

The quality of this course in the series is a far cry from that of module 1 and 2, which is a shame because this is the one that I was really looking forward to. The professor does not seem comfortable and uses a lot of extra words in his lectures which can make them confusing and rambling. Many questions on the quizzes and assignments are not covered or well explained by the material. Many assignment questions have to be explained by teaching staff on the forums because the task is not clear.

Week 1 was great...and then it all went downhill.

Too much material cramped into 4 weeks. The lectures are monotonous and rarely go in detail and provide real world cases. yeah, the data is from the real world but just punching code without explaining it is not very instructive.

Oh yeah, and lets not forget the last time the course has been updated was in 2017 and none of the bugs that keep popping up with the code and the autograder have been fixed.

Sorry by bad writting, english is my second language, but:

Again, the videos and suggested reads are not sufficient to learn all that is needed in assingments or in real life application. Doing others courses in coursera like courses offered offered by University of Macquaire turn more clear that this course is so hard to learn because there's less things that what is actually the subject

I was excited going into this course because the others in the series were taught well and I had learned a lot. Unfortunately, this course greatly disappointed. The lectures were dull, included a lot of mistakes, and did not cover most of what was expected during the assignments. All in all, this course was a waste of time versus just learning scikit-learn on your own.

Not happy & satisfied with the assignments. Whenever I tried to submit, always error occurs, mostly files does not exist. Went to forums though, but files are kept elsewhere, especially for Assignment 4. Had to specially download the file and uploaded in the project directory just to work. Need to have proper file arrangements before starting the assignment.

I am not a big fan of this course. The assignments were too easy up to the last one that was too hard. There is no moderation in the forums, you can ask a question and nobody will answer.

The instructor makes me sleepy. The autograder runs outdated versions of many packages and was last updated in 2018. Although the mentors are always active in the discussions forums.

I do not have the impression after this course that I have reached a level of familiarity that I will continue using the content of this course. Disappointing.

great topic, poorly presented. material not well divided among weeks. lots of repetitions. lack of hands on practice until the very last task.

Autograder is poor and professor is hard to listen to. You're better to just do a YouTube tutorial, like Codebasics.

lectures are boring, not updated but yes i learned something, but its not up to the margin

Desperately in need of an update as much of the code is no longer up to date/broken.

Instructor tell the thing which are far beyond from asignments and quizes