Dec 07, 2018
It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations
May 07, 2019
I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative
By Junhwan Y•
Jun 30, 2019
This course include deep contexts about Machine Learning. But, It's somewhat boring.
Jun 30, 2019
Some errors in Kubeflow quicklabs.
By Mirko J R•
Apr 02, 2019
Jul 10, 2019
By Melissa K R•
Dec 11, 2019
I was really hoping I'd gain some real practical skills and knowledge about the different aspects of building and deploying a machine learning model on GCP. Even though a lot of real estate was covered in this course, most of it was theoretical, and I cannot say that I "really" learned how to implement them if I were working on a big machine learning project, which was exactly why I took this course. The only labs that had some practical aspects to them were also disappointing; one only looked at a number of Java modules and the other was a demo of Kubeflow that I couldn't follow at all, and was different from the lab itself! First off, the fact that Java was used in the first instance took me by surprise, and I wonder why the same thing couldn't be accomplished with Python. I have zero knowledge of Java and that was uncalled for, but tried very hard to make sense of the code. But I won't definitely be able to write it myself. And in the case of the last demo, I simply couldn't understand what the instructor was doing and where!
I expected a much much much higher standard from this course, but overall it was quite disappointing and I cannot say I took anything away from this course other than some theoretical concepts about various subtleties when it comes to ML on GCP! It would've really really helped if there were more actual lab work included in this course, just like the previous one and each concept was accompanied by one such hands-on lab, and concepts were explained step by step.
The other thing that was very odd to me (and is the same for every other course in this specialization) is that a ton of material is squeezed in Two Weeks. It would've helped if they were separated over multiple weeks. This change in the organization of material would really help learners to visualize the flow of topics. Right now, it all seems a load of crammed topics that have been merely glossed over!
By Alireza K•
Sep 29, 2019
The Qwiklabs should be more than copy pasting commands. Also I think this course is suitable for people with many years of experience in software development not people like me just came out from university!
Jul 05, 2019
This specialization consists of 5 courses:
Course1: End-to-End Machine Learning with TensorFlow on GCP
Course2: Production Machine Learning Systems
Course3: Image Understanding with TensorFlow on GCP
Course4: Sequence Models for Time Series and Natural Language Processing
Course5: Recommendation Systems with TensorFlow on GCP
In specialization's FAQ say nothing about "audit" option. Are You know what is it ? "Audit" means that You can use course video material even after You subscriptions ended.
By fact, only "Course 1" has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization:
courses 2-5 has special items in FAQ:
"Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
"Who have paid" means that after You subscriptions ended, you lost access to video materials in this courses.
1 star only for "Audit", content and lecturers are rated higher - at least 4 stars