This was a demanding course, requiring one to listen to many long lectures, do a lot of extra reading and research online for more in-depth machine learning knowledge in order to answer all the questions and problems. It was time intensive yet provided a good background on machine learning models.

Great course. It doesn't confuses you very deep mathematics involved in machine learning. Rather, with a touch of it, it focus more on how and when to apply the models in Machine learning. How to evaluate and optimize them. It's really Fantastic with it's hands on projects in assignments.

This course is very cool and interesting. One thing, it would be more useful for me to have a little test/exercise after or in the middle of every video - to try, how I understood the material. Like in Andrew NG course or in Text Mining.

Anyway, thanks for a great course and your work!

Great course, somehow assignments are not always on the same level, the first was easy, the last seemed to be very complex, but was not, the assignment instructions were misleading. Anyway, I enjoyed this course too much and I want now to improve my abilities in underlying theories.

Not only in the last week, all the weeks can have assignments which are like projects. That may give more sense of analyzing and understanding the process of model selection, application of supervised learning techniques. But the course is good, and i have learnt it in faster pace.

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

Lectures teaches you about the various ML algorithms available. Quizzes are challenging and lab assignments are simply an application of the libraries. Lab assignments are not that challenging but you need to be good at using pandas/numpy. Overall, it is a good course.

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.

As compared to the previous courses in the same series, the content felt longer and slower. There were also issues in many videos that had to be corrected using pop-ups. It would be wise to fix the videos for future students. Everything else was great.

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.