Apr 26, 2018
It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea
Apr 17, 2020
Awesome!...Exciting!.Thanks for such an interesting hands on course..really appreciate all the tutors for all the valuable knowledge and helpful responses.
By Jair M•
May 22, 2019
Some videos are missing, but anyway is a great course
By Amalka W•
Dec 29, 2019
Course covers scalerble deep learning concepts
By Andrey O•
Sep 07, 2018
Part with DeepLearning4J is not very good...
By Vinayak B•
Jul 30, 2019
Really Helpful course for AI Enthusiasts
By Mobassir H•
Apr 22, 2020
pytorch instructor was the best <3
By Valerio N•
Mar 27, 2019
Very Complete course.
By Arati Y•
Apr 09, 2018
It was nice
By Tobias H•
Aug 26, 2018
By Pierre-Matthieu P•
Nov 30, 2019
I was pretty disppointed overall.
Pros : we see many types of tools and get to use some of them in the programming assignments. I feel like I now have a general knowledge of the field. I particularly liked the aspects of scaling and deploying models in production.
Cons : This honestly feels more like a rough draft than a finished and polished course. I would have liked a consolidated overview of all these tools, their pros and cons, etc. Some tools and techniques were explained in literaly 15 min(!) and in some cases simply walked through a tutorial from the tool's website (!!). A programming assignment was broken through not being updated for more recent spark versions. Some videos mentioned a non-existent programming assignment (I assume they were reused from an internal IBM training session), etc. The comparison with say Andrew Ng's course on ML is cruel.
By Jakob S•
Mar 26, 2020
The course covers some very interesting and important concepts, however on a very low level. The reason for this might simply be the lack of time; one cannot properly cover methods for AI image processing, NLP, etc. in such limited space. I also had mixed feelings about the exercises: It is very nice to see applications of the tools discussed in the lectures, but unfortunately the exercises are so simple that they can be easily finished without really understanding the code.
By Jose L M G•
Apr 01, 2019
Lo hago, el curso es muy bueno en cuanto al uso de la plataforma watson, pero falla en explicar los fundamentos principales con animaciones, ejemplo, el curso de pytorch de udacity enseña eso muy bien. En lo demas esta bien, pero al no contar con elementos visuales de ayuda en laclase de LSTM se hace tediosa.
By Jeet D•
May 12, 2018
The course is very resource heavy, i.e. it has great intuitive resources, but the learning experience was very poor. Some of the instructors were very sparse with the material contents, some just brushed over the contents without much explanation and.
The quality of the course has to be improved.
By Daniel P•
Jul 10, 2018
Too much focus on IBM platform, good overview on Keras/SystemML/DL4J though, some presentations could have been better prepared and implemented. Overall an average Coursera course and not a particularly great experience to work through the material.
By Eugene N•
May 22, 2020
Something happened to the free 1CPU 4GB python environment on IBM watson studio. It is unavailable and so I had to struggle with Skills Network Labs instead. Please can this be checked?
By Ceren A•
May 10, 2020
Several lectures were superficial. I feel like I need to put a lot more time on my on to understand how to build a proper neural network model.
By Mark B•
Apr 17, 2020
Hard to follow ... found a lot of assistance in discussion forums
By Raqui M•
Apr 12, 2020
unfortunately the time series chapter is not complete
By Francesco d C•
Nov 21, 2019
The lessons provided by Skymind were very poor.
By Csaba P O•
Oct 01, 2019
I liked the general idea of this course, but the actual material is not as good as it could be. There are lots of inaccuracies in the material (like annoying typos and not working code examples) which should be corrected before you sell this course on Coursera.
I strongly suggest that you go through your material with someone who has pedagogy knowledge and who can assist you to improve the didactic aspects of your material.
I did this course (and the whole specialization) for the practical examples as I feel rather confident with the theoretical aspects of machine learning, but I wanted to learn how to do these things in Spark environment. At the end of the day I have got what I wanted (more or less, as the NLP part was really lousy), but if I would not have strong experience with the field, I would have been surely lost. Honestly, I would have a hard time to recommend these courses for someone who wants to learn about machine learning and not about how to do machine learning with Keras, etc. And I am sorry to say that, because, again, I liked the team, the attitude, and the technical aspects of this course.
By Eric C•
Apr 29, 2020
There was a lot of interesting content, but I was sad that the programming assignments were fairly trivial. Any point where something deep and useful could have been assigned (I was hoping to get experience or guidance on building an LSTM autoencoder, for example) we were instead given a super easy alternative that was mostly pressing [Shift]+[Enter] on a Jupyter Notebook. SystemML seemed cool, but the only thing we ever did with it was multiply some matrices, and not even on a Spark cluster. I felt like I didn't learn that much because there was no point where I really had to engage my brain.
By luca t•
May 17, 2020
Some lessons are too hard to follow because of stong foreign accents and poor grammar, so the student ends up spending most of their effotrs on tranlation. Instructors mostly go through coding notebooks quite quickly. The oil forcasting module is an example of the defects of the course: Poor language, poor presentation, 'here', 'there', 'now' are used to reference the code with no pointers, slides are borrowed from online sources and insufficient. The proposed code is a tentative time series forecating toy example with catastrophic results, not at par with any time series standard.
By Leonardo I•
Aug 28, 2019
The course is delivered at a very high level of abstraction. If you are a beginner, I wouldn't recommend this course as the explanations provided are quite vague and not so good in many instances. Justifications for the use of quite a couple of algorithms/values are not provided thus leaving the learner with a lot of "Why's"
One of the nice things about the course is that the instructor responds promptly to students' queries.
By Sheen D•
Sep 01, 2019
Again, the instructor speaks way too fast to explain anything. Even the subtitle cannot follow the instructor line by line. Frequent occurrence of inaudible words or sentence or wrong translations. When it comes to the code, never really understood what each line of codes is for...
By Quang A•
May 25, 2020
It is difficult to fully understand the contents of the lesson, too many theories and not yet associated with practical problems. It's like studying in a university and for those who have more knowledge of math, not for everyone.
By Jorge A V•
Feb 05, 2019
Explanations are a bit rush. Would not be easy to follow if I would not have deep previous understanding on the Deeep learning topics.