Mar 01, 2017
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
Jun 18, 2018
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
por Brian F•
Aug 16, 2017
There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.
por Kyle H•
May 09, 2018
A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.
por Léa F•
Jan 09, 2018
Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.
por Hongzhi Z•
Jan 03, 2018
All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.
por Max M•
Dec 12, 2017
Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.
por Francois v W•
Dec 10, 2017
The course gives a decent overview of the model building process and covers a good spread of machine learning methodologies. I found that the videos focused too much on some basic/immaterial concepts at times and tended to gloss over the more in-depth or complicated sections. It would have helped if difficult concepts were explained with more examples. This meant that a lot of self study outside the lecture notes had to be done. The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.
por Matthias H•
Mar 26, 2016
The quizes do not match a 100% with the lecture videos. There are some weird questions. My algorithms' outputs deviate from answers some times, which is due to different software versions. Quizes are not very educating this time. Courses by Brian Caffo were much better.
por Christopher B•
Mar 01, 2017
While the overview of the content seemed very reasonable both in scope and pacing, the lack of swirl exercises meant that the final project for the course was a bit jostling. Overall, I think this course still needs some development in the way of exercises to familiarize the student with the practical exercises associated with machine learning.
por Andrew W•
Mar 13, 2018
Very interesting subject area, I think there is simply too much to cram into one course. Should consider spliting the subject into 2 courese or simply concentrate on only 1 or 2 main areas (e.g. cla
por Christian B•
Apr 30, 2017
First I want to thank very much the instructor in the online forum. He helped me a lot at the end of the course and his tutorials for gh-pages are excellent. He was also very fast in responding. Thank you.
The course did ultimately not really gave me what I was looking for. Maybe too may different facts and not enough depth. I am not sure that I can confidently say that I can build a ML model now. Technically I can, but the deeper understanding is missing. For example: When would I use which method (for example rf versus naive base), the last exercise about cross validation was not fully clear. Using the caret package is too high level for a learner. It would be better to see some more step by step examples. It was not clear to me what the expected error calculation in the last exercise was really looking at. Maybe what is missing a swirl exercises, not using caret. and then explaining how caret can simplify it. We also learned how to create a predictive model, but did not go into how the model gets updated and gets retrained, an important aspect of ML. i also do not see unsupervised learning to be covered.
por fabio a a l l•
Nov 14, 2017
Poor supporting material in a course that tries to cover a lot in a very limited time.
Nov 14, 2016
Although again very interesting, I found the lack of additional materials such as practical exercises, swirls and a book reduced the depth of the course knowledge for me. Maybe we have been spoiled by the previous courses :-)
por Eduardo P•
Apr 14, 2017
This is such a cornerstone topic to the Data Science Specialization that I think it deserves a better designed and more polished curriculum. The subject is so extensive that it might be worthy to split the contents in two courses. Finally, I would like to suggest the authors of the course modeling the curriculum following the amazing treatment of the subject found in "Introduction to Statistical Learning" by Hastie, Tibshiriani et. al.
por BAUYRJAN J•
Mar 01, 2017
Instructor rushes the course and does not explain much in the same level of details as respective quiz requires
por Christoph G•
Dec 04, 2016
The topic is too big, for one course from my point of view.
por Andrew W•
Feb 10, 2017
The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.
por Rafael d R S•
Jul 24, 2018
this course seemed too rushed for me, too little content for such a extense subject
por Ehsan K•
May 30, 2019
This is a good course for someone who has already done the previous courses in this specialization series.
It covers the most basic ideas in machine learning and expose you to work on real problems and learn by experience. if you are looking for more advanced in-depth courses, you need to take other courses as well.
Overall, lectures are in very fast pace and as a result they have several mistakes in them you should be careful about.
por Jorge B S•
Jun 25, 2019
I have passed 5 courses of this specialization and I am not fully satisfied with this one. The course is a very brief introduction to practical machine learning, as the concepts are explained very fast and without a minimum level of detail. Then, most importantly, there are no swirl exercises, so it is quite difficult to put the acquired knowledge into practice. The other 4 courses I took, they all had swirl and that was great. Nevertheless, the course project is quite nice in order to face a real machine learning problem.
por Rok B•
Aug 08, 2019
The material is well choosem but poorly explained. This course among all would need swirl excercises, or just more excercises in any form. Instead the lecturer rushes through the material. So in the end you do have some overview about machine learning in R but not enough hands on experie
por Manuel E•
Aug 08, 2019
Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.
por Davin G•
Aug 26, 2019
It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.
por Yohan A H•
Sep 06, 2019
I think it was a very fast course and I feel more real examples would have been useful,
por June K•
Sep 05, 2019
This course does not have the depth it needs, but I do learn a few valuable things. I suggest breaking this course into 2 courses and give more lectures on using caret package and other packages as well. Another thing is I could not ever find the correct answers for the quizzes, and most of the time has to guess and take the quizzes 3 times to get things right.
I invested time and effort in doing the last project; but got a not so good grade due to peer review process. I got every requirement done and even have a direct link to my HTML final report but 2 out of my 4 my peer reviewers have limited knowledge of GitHub could not find my link to HTML file. That said with a higher level courses, peer review process has to be different.
por Philip E W J•
Jan 30, 2019
Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.