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.
par Alexander R•
Aug 21, 2017
Very basic, might as well just read a cheat sheet. No explanation of how or why to choose different options in a pipeline, for example, which data slicing to use (k-folds, bootstrap, etc). Just runs through how to do them.
par Pawel D•
Jan 22, 2017
This course is rather bad, not well rehearsed and hastily delivered. Especially in comparison with other, in-depth course of this Specialization. The course is more of a 'caret' package review then actual Machine Learning. I learned how to use the
par Michael S•
Feb 07, 2016
Had big expectations for this one... really one of the ones to look forward to after working through the beginning of the specialization, but for some reason, it seemed any prof or even TA interaction was absent this time around like in none of the other specialization coursed to date. Bugs in the new interface and quizzes weren't really addressed. Couldn't even get an official response about the apparent removal of Distinction-level now (which I'd been working to get in all specialization courses and now seems no longer an option). Still interesting content. As a "free" course, it's still really valuable. As one of the people that paid for this and all others in this specialization, this is the one I felt didn't return as much value to justify the payment with no "official" course staff seeming to be involved this round.
par Damon G•
Mar 02, 2016
The mathematics in this course are at a high level (similar to Statistical Inference) - and are presented at a pace that is challenging without significant background in the field. There is little guidance presented on the methods required. It is recommended that students source out plenty of support material (intro to statistical inference and similar).
par ANDREW L•
Jan 27, 2016
Does not give much intuition around the subject. I found the lectures a bit uninspiring. Lots of powerpoint (just text, no images or visuals really) and the lecturer just underlined the words he was talking about as he read the powerpoint out. I found the Udacity Intro to Machine Learning course gave a much better intuition and understanding of this subject. We also had slides on how to split data into a training and testing set on pretty much EVERY lecture - what a waste of time!
par Tim A•
Oct 14, 2016
This is a part of the data specialization; from afar, I would not be interested in Machine Learning because of this course. I will seek other methods to learn.
par Arcenis R•
Feb 26, 2016
The instructions for the final project were very unclear and even though I submitted all assignments well before their respective deadlines and reviewed the required number of projects my work was not processed for a grade thereby delaying my specialization completion.
par Marshall M•
Sep 23, 2017
A lot of the concepts in the course are grazed over very briefly and don't go into that much depth. In addition, some of the concepts are taught as concepts, they are taught through examples which tends to contextualize the material. Good content but could be put together in a more in depth manner.
par Felipe M S J•
Dec 02, 2016
No es un curso en el que se aprenda demasiado.
Parece demasiado avanzado en el uso de "caret" y en vez de enseñar, parece ser que todo debe ser aprendido con anterioridad.
Todo el material adicional que se necesita en el curso, es en general contenido externo.
Jan 18, 2017
not what I expected for a machine learning course
par Jonathan O•
Apr 18, 2016
I saw two main issues with this course: 1) dated lecture videos, oftentimes with R code that can't be replicated using up-to-date packages, and 2) lack of thoughtful design: example after example after example after example doesn't really teach you anything.
par Vincenc P•
Mar 31, 2016
Course content feels upside down. You'll learn about machine algorithm specifics and caveats before anyone explains what the said algorithm actually hopes to achieve.
par Jeffrey G•
Sep 12, 2017
Course project was the only project work, needed more. This course should also use swirl(). Quizzes et al contained mistakes.
par Mehrshad E•
Mar 28, 2018
This course really lack something like SWIRL. The lectures only provide a summary, which is not helpful for someone new to the machine learning. Also, the instructure tries to cover pretty much everything but not in depth; instead, I think fewer topics should be covered in depth.
par Anju M•
Apr 17, 2016
Felt difficult in understanding the overall course in short duration . 1 month is not enough for this course. I request the authors to make the course much more simpler
par Yang L•
Aug 14, 2016
needs more case studies and examples
par Allister G A•
Dec 25, 2017
The course needs to elaborate more on hands on discussions.
par Stefan K•
Mar 10, 2017
Very shallow content - broad, but not deep. Not many assignments instead of the last one. We hear what we heard before. For the same price, Analytics Edge at EdX is far better choice for practical machine learning.
par Peter G•
Feb 28, 2016
Absolutely useless random un-explained list of facts and advices that is thrown to a learner without any attempt to give a systematic approach. Pure waste of time and effort. Can only be suitable to those, who already know the subject well and can use some additional facts that are randomly presented in this "course".
par Norman B•
Feb 07, 2016
This is too high level for a machine learning course. You don't exactly learn a lot about the techniques just how to use them and name them out if you're having a conversation with a person. My least favorite course in the series
par Michael R•
Jan 19, 2016
lecture can be really unclear sometimes because lecturer breezes through the actual implementation of training/predicting: "use x, y, and z [underlines some stuff on screen]" and you're done
Also lots of mistakes/typos in lecture and quizzes
par Agatha L•
Jan 23, 2018
I was disappointed with this course. For better or worse ML is a part of data science and, in this course, the instructional depth was lacking. The lectures provided examples of how to implement a few ML algorithms in R, with very little actual instruction on the intricacies of these algorithms, theoretical foundations etc. Taking the course I felt somewhat cheated (a google search would have done the job of the class), and frustrated with various little bugs in Quiz/Assignment content.
par Haolei F•
Mar 13, 2016
Need to get more in-depth
par Y. B•
Feb 06, 2016
incomplete and not clear. extremely disappointed.
par Stephen E•
Jun 27, 2016
To be honest I don't think this is worth the money.