great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.
This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
By Sridhar V•
This course was very interesting. Probably the longest course (duration wise) in this specialization. This course had to cover a lot of ground in 4 weeks time. Thoroughly enjoyed the assignments and it was challenging as well!. Gave 4 star because there are minor problems wrt. Autograder. But content wise there are no complains.
Lectures were a bit slow, I personally felt pace could be increased and more content could be covered in areas like boosting and all.The assignments gave me a hands-on approach in using sklearn library.I felt it was over-all a very good course and would definitely recommend it for others.
By Chaitanya D•
Interesting course, was curious about what all things will be covered in this course. It touches most of the topics that one should be aware of ML. Only thing that I felt bit overwhelming was the amount of material which was covered in 4 weeks. Could easily be stretched to 5/6 to make it less demanding for a novice person.
By Marcin B•
Good stuff :) However approaching final assignments I was missing more info about preparation of an input data. As far as I know it is to some extent covered by first course of entire Specialization. So, I plan to take this one as well. But overall - very good intro to ML in my view. Thumbs up University of Michigan :)
By Alan E•
Great course, with a very practical overview of the different options available for machine learning models using Python. The concepts are the same as in R-based machine learning, but this course was great for getting experience with which Python functions to use for various machine learning models.
By KUMAR M•
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.
By Elizaveta P•
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!
By Amina B•
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.
By Lalitha G•
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.
By Lu E•
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.
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
By saikanth g•
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.
By Daniel W•
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.
By Amit P•
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.
By Donald V•
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.
By Cole M•
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
By Al W•
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!
By Siddharth S•
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.
By Varada G•
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.
By Sparsh B•
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
By Mark S•
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.
By Xuening H•
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.