Oct 14, 2017
Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!
Sep 09, 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
Apr 28, 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.
创建者 Maxwell's D•
Jun 23, 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!
Jun 06, 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
创建者 Çağdaş Y•
Oct 22, 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 :)
创建者 Mohit K•
May 24, 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.
创建者 Samchuk D•
May 30, 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
创建者 Sridhar V•
Jun 12, 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.
Feb 16, 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.
创建者 Chaitanya D•
Jul 04, 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.
创建者 Marcin B•
May 26, 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 :)
创建者 Alan E•
Feb 05, 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.
创建者 KUMAR M•
Nov 26, 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.
创建者 Elizaveta P•
May 15, 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!
创建者 Amina B•
Jun 12, 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.
创建者 Lalitha G•
Nov 06, 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.
创建者 ENZHE L•
Nov 07, 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.
Jun 16, 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
Apr 13, 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.
Jun 08, 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.
创建者 Daniel W•
Jul 09, 2017
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.
创建者 Amit P•
Dec 26, 2019
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.
创建者 Donald V•
Dec 18, 2017
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.
Sep 08, 2020
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.
创建者 Cole M•
Aug 30, 2020
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
创建者 Alex W•
Nov 18, 2019
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!