8. Sep. 2017
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
26. Nov. 2020
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.
von HRITVIK S 1•
13. Juli 2020
The course is designed perfectly and the pace is such that beginners in machine learning would enjoy. The course was well structured out and in a span of 4 weeks I think i learnt a lot. The only limitations i found were with the autograder not detecting files and other minor glitches like the videos not being marked completed even upon completion. But those can be fixed easily.
28. Apr. 2020
Just like other couses in this specialization, this course has great assignments which help alot.
As to instruction, totally different to previous courses, this instructor covered almost everything, probably too much for a four week course. I think I start to have some sense of machine learning however, I do need more study, probably Andrew Ng's course and refresh my maths.
von MAULANA R H 2•
25. Apr. 2022
Materi dan penjelasan yang diberikan selama kursus sangat bermanfaat dan mudah dipahami dengan baik, ditambah dengan pembawaan materi yang terstruktur mempermudah dalam memahami penggunaan dari setiap algoritma dan parameter yang ada. Kekurangan yang ada hanyalah di bagian penilaian assesment yang bermasalah diakibatkan dengan file yang tidak terbaca.
von Calum M•
16. Mai 2021
I learnt a lot in this course. The lectures, assessments, and reading material were all top-notch. The forums are immensely helpful. However, I'm giving 4-stars rather than 5 because I spent more time than was necessary in overcoming autograder issues. My suggestion is to improve the autograder so that assignments can be submitted more seamlessly.
von Maxwell's D•
23. Juni 2017
I really got a lot out of this course. I started with a solid background in traditional data analysis (PhD in experimental physics), but knew nothing about ML. This was a great overview, providing a just the right trade off between depth and breadth--plus it was short, which is good. I can now go and do deeper dives into the material. Thank you!
von Felix H•
16. Jan. 2021
The combination of assignments and lectures worked niceley for me. Good feedback on the discussion forums, too. Only thing which should be improved is the auto grader. The course introduces a lot of algorithms, but also gives you insight into how to evaluate their performance. In the final assignment it all comes together, which is always nice :-)
6. Juni 2019
I think it gives a great overview on Machine Learning and Sklearn. Nonetheless i noticed it is less curated compared to the prevoius courses in this specialization (wrong filenames, unfunctioning links, old version of pandas respect the one used till now). Anyway it worthed and I'll give a look also at the optional unsupervised learning part
von Çağdaş Y•
22. Okt. 2017
The teacher's voice is not motivating, it made me fall asleep all the time. But content is surely good. It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices. Seriously, speaker needs to speak more alive! I don't want to hear deep breathe noises when watching a course :)
von Mohit K•
24. Mai 2019
I Took this course blindly without knowing much about data visualization libraries. It took me a month or so to learn them first and then attempt this course further. The course study material is very decent but the assignments are pretty good and tricky. It is definitely a must-go-for course and I would surely recommend to my colleagues.
von Samchuk D•
30. Mai 2018
This one is very good and informative.
Although there is no explanations how to decide what type of preprocessing do on data set (to choose whether or not to do winsorization, convert categorical features to one-hot for linear models and to labeled for trees, etc) it still very helpful in understanding of PRACTICAL part of machine learning
von Sridhar V•
12. Juni 2020
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.
von Narendhiran R c•
16. Feb. 2020
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.
von Chaitanya D•
4. Juli 2017
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.
von Marcin B•
26. Mai 2020
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 :)
von Alan E•
5. Feb. 2018
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.
von KUMAR M•
25. Nov. 2019
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.
von Elizaveta P•
15. Mai 2018
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!
von Amina B•
12. Juni 2020
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.
von Lalitha G•
5. Nov. 2019
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.
von Lu E•
7. Nov. 2017
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.
von Ryan M•
26. Juli 2021
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.
16. Juni 2017
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
von Loi H H•
10. Juni 2022
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.
von saikanth g•
13. Apr. 2020
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.
8. Juni 2020
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.