Learner Reviews & Feedback for Applied Machine Learning in Python by University of Michigan

kind of a good course. However, I think too much things have been put into this four-week class. All methods, for example, random forest method need a lot of practice. In the four week, I think I am not familiar with most of these method and I need to practice more in the future.

I've learned a lot of basic concepts about common machine learning models and how to apply these tools using python. Although practices and deep understanding are still not enough, this course is really great and worth learning for beginners who want to learn more in this area.

This was a very practical course with a lot of useful stuff! My main frustration was that the final assignment could have used more starter code, as I spent way more time trying to get the data to load properly than I did on finding a model to score high enough for full marks

Totally nice course,As it is Applied Machine Learning all lectures do not go deep and just touch on the topics.Did not face any issue with autograder this time but its better to use newest version of jupyter notebook.The teaching staff were highly responsive.

The course was really well constructed, but there wasn't much to teach in it like just use this code and get the values.

I strongly feel that all the assignments should have been like the assignment of week 4.

None the less, it was a great learning experience.

Pretty good. I really like the quality of the notebooks provided. Also assignments are interesting.

I would improve quizzes. Some questions were really hard to understand or misleading.

Also, I would really love to learn more in depth about the algorithms.

This course is an excellent run through of the pipeline for developing, running and evaluating machine learning models. The video lectures were monotonous and long, though. The last assignment was especially meaningful and enjoyable. Highly recommended.

If I could I would give this course 3.5 stars. Most of the coverage of the concepts in this course were pretty light and there were several issues with the autograder being difficult that made this course a lot less enjoyable than it could have been.

There were a few mistakes in the assignments which causes unnecessary time wastage on student's end. Otherwise, it was quite a good course.

Also including a demonstration of encoding textual data while implementing Random Forest would be helpful.

Good practice content and good explanations. Some of the content I would rate as great. There could have been more smaller programming exercises that built up to the main exercise for each week. This is the only reason I did not rate as 5 stars

Lots of minor issues with the Jupyter notebooks that could easily be fixed but the instructors just post a way to solve the problems in the discussion form instead which is frustrating. The material itself was extremely interesting and useful!

It would have been wonderful if the notebook codes were written and explained in the video the same way as in earlier courses in specialisation taking care of the implementation details as well.However still a Good Course of the Specialisation.

It is a bit dense - be prepared to spend more time working through examples - and reading the reference book. The lectures, unlike the previous ones in this set, does not allow time for you to practice with the examples in jupyter notebook.

This course was really helpful in understanding the working of various machine learning algorithms.

I was able to gain understanding of various evaluation techniques and there usage in different scenarios.

Thank you for this wonderful course

Lots of useful information, but sometimes the content could have been better explained. Too many errata than necessary in the assignments at the end of each week. I found that the Jupyter notebook would stop working after about an hour.

Pro: I really like all the homework. The data is dirty and the work is a little bit challenging but doable.

Con: I prefer more animation in slices during the lectore to keep me concentrated. I get distracted watching the lecture's face.

I learned a lot about machine learning with python and would definitely recommend for someone with decent python background.. Some of the assignments have some very unnecessary technical hurdles that are unrelated to the material.

Very hard but worth it. I only took one start off because I did not like the professor. Very sleepy voice and not very exciting explanations. Material was excellent and very helpful for the completion of assignments and quizzes.

I completed this course in specialization and this is the only course which is worth of your time, rest two before this course were your head against a wall.

Excellent course with all the understanding a student need.

Thanks :)

Really good and applied course. It teaches you a lot of powerful tools for machine learning.

The only negative thing is that the week 4 cover hard topics, and the explanations are vagues sometimes, but nothing too terrible.

Very happy with the course content.

On the other hand, certain instances need to be updated/corrected.

For several assignments, the files don't load and you need to dig through the forums.

It would've been 5 stars otherwise.

It can be more detailed. It is on broader terms only. I will recommend Andrew Ng ML course to do as well because it covers too many things than this module. Otherwise, this is a good module as well. :) Enjoyed doing it.

Good Introduction Courses, but need more guidance for assignments as there is a gap between two of them. Assignments do need some more hours to finish. In all, a great course for anyone to break into machine learning.

Challenging and fun course. The number of topics is on the high side. Maybe break this into 2 courses? The programming assignments are fun. You will need to go to discussion forum to solve often encountered problems.

Much better than the second course, the materials are carefully prepared and organized, teaching staff are very helpful in solving issues, however, assignments are not so challenging, still needs improvement.