I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
by Jalis M C•
by Debasish D•
by Sajal J•
by Benedict B•
by Shawn P•
by Daniel S•
Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.
In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.
The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.
TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.
by Gil F•
Notwithstanding the great video lectures this course's assignments were poorly composed:
Firstly, there are no programming assignments! I understand the material here is mostly conceptual, however subjects such as 'Transfer learning' and 'Multi - task learning' should be given as a programming assignments. In 'Transfer learning' you need to modify an existing model, which I think is a good tool for a student. Hopefully we will use it in future lessons. Lastly some of the questions in both 'quizzes' have many complaints in the forum and the same complaints reappear yearly, therefor it's a bit annoying no measures are taken to modify the questions so they will be clearer.
by Alexander D•
This course was pretty poor. Too many of the lectures are repetitive, and the examples given to discuss the concepts seem overly simplistic. It would be far better if AN actually discussed previous cases and what pitfalls to watch out for. For example, it's useful for practitioners to understand human component features that he mentions. He's probably seen a lot of instances in which engineers came up with great ideas that ended up differentiating a mediocre-performing algorithm from a far better one. He could also discuss go into greater case study detail of instances in which transfer learning/muti-task learning worked well or not.
by ananth s•
Very verbose with hand-wayy examples. The 18 minute lecture was the hardest Ive tried to not fall asleep. The second quiz has extremely badly written questions with multiple choice answers. Very ambiguously worded QnA. Don't mistake this review for the whole DL specialization though. Andrew's DL specialization course is brilliantly structured and an excellent primer for folks such as myself just getting into DL. It is only this section on structuring ML projects which is a little bit of a drab.
by Younes A•
The material is great, but the production quality is so poor that I had to give 4 stars only. Videos have blank and repeating segments, and more quizes have mistakes that make getting a 100% because you know the material impossible (you have to tolerate some wrong answers to do it). This means you can't rely on quizes at all, because maybe the ones you got right were actually wrong :). The ones I got wrong were also called out by other people on the forums, so I guess maybe I am right.
by Gonzalo G A E•
This course is just a set of (perhaps useful) advice on how to make decisions when working on a project, not a course on techniques or how to actually do things. There are no programming assignments as in the other courses of the specialization, just some "decision making simulators". I learned more and enjoyed more the other courses. It feels like all these advice could be given as part of the other courses. (But perhaps I am much more technically inclined.)
This part did not interest me much because I find that it does not go into detail and concretely I did not learn anything useful. Indeed we have plenty of examples that teach us what to face in a situation but in the end if we are a beginner we simply do not know how to do ... I find that it is + a documentary that Classes.
I am hard on my scoring of this 3rd part but I strongly recommend to follow the first 2 parts which go into detail.
by Miguel A M•
Although the content may be useful for Deep Learning researches/practitioners. I think there is no need to have a stand-alone course but rather include these guidelines or best practices in the first two courses of this specialization. Some of the concepts are as well repeated. There are no programming assignments or any other way to 'visualize'/'practice' the ideas mentioned here.
by Guilherme Z•
The most exciting part of the course as others in the series is the interviews that Andrew does with deep learning researchers. I thought I would learn more about how to structure actual machine learning projects from a software perspective and how I would incorporate them to real products. I felt the videos for this course were too long and cover somehow basic common sense.
the course doesnt have any programming assignments. I feel that these two weeks should have been added/combined with first 2 courses. The knowledge that is provided is useful, but it is mainly useful once you are an expert at building neural networks and models. I feel that this course should have been the last course in the series instead of the 3rd course
by Markus B•
Just a few videos without any programming excercise or a bunch of rather broad statements that are not really tried out in programming examples are not really worth the money and more importantly the time. The first two courses are good, this is definitely a drop in terms of quality. This one needs more meat on the bone.
by Jim M•
This had the potential to be a very good course, but fell far short, in my assessment. It probably should have been rolled into the previous course as a couple of additional lectures.
Either that, or it should be expanded greatly, with more practical exercises to solidify the concepts taught.
by Ted S•
Doesn't look like it was checked for quality control (e.g. Videos with bad takes), Ng rambles sometimes so that it seems as if he is filling time, there are no knowledge checks. This course wasn't ready. Case study flight simulators are good, but poorly introduced.
by Tiffanie B•
The teacher seemed to not have a clear idea of what all he needed to say in the video and verbally flailed somewhat, and many times seemed to be adding things purely for the sake of padding video length. Don't waste my time. The content was mildly useful.
by HAMM,CHRISTOPHER A•
I need a lot more practice than is offered here. I would also strongly prefer if the instruction followed some of the best practice laid out in books such as "How Learning Works" because I have difficulty following the instructor's line of reasoning.
by Leonardo M R•
Answers in the multiple choice seems incomplete for me, I don't necessary agree with the answers presented unless more detail on some context (that I don't think we should assume) is present for the questions.
by Пильгуй В Л•
This course is good, but here is so few practice. This is hard to understand without practice. Looks like I didn't understand many problems in this course. Need more explanation, more samples, more practice.
by David L•
Zero programming assignments, but simple quizzes that will make whatever you just learned as fleeting as the morning dew on a hot summer's day. Too bad, because otherwise the material is quite interesting.
by Mahesh B K•
Although important, i think this should be the last course in the specialisation as it covers the harder parts of handling various errors and their causes before knowing how these models are trained