8 апр. 2020 г.
This course covers a lot about the data pre-processing, and the tools available in Google Cloud to enable the gruelling tasks. Thanks very much for the lectures and training labs. Very informative.
25 нояб. 2018 г.
It's a pretty interesting course, specially that's the only one that teaches featuring engineering with a focus on production issues, but it assumes some knowledge with apache beam, and dataflow.
автор: Yasim K•
12 сент. 2018 г.
The tf.transform and Apache Beam concepts are not explained in simple ways.
Also the lab jumped from simple programs to complex programs.
автор: Robert U•
11 июня 2019 г.
The assessments do not actually require writing any code; you just execute the given code blocks. Little knowledge will be retained unless students actually write code and solve problems, even for the motivated ones who read through all the given code.
автор: Stephen R•
26 авг. 2018 г.
A lot of the code, did not work.
автор: Mike W•
22 июня 2019 г.
The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.
автор: Adrian H•
10 мая 2020 г.
A lot of the labs need updating and revising and made more meaningful.
автор: Martin A K•
31 мая 2019 г.
Would appreciate more guidance on the exercises
автор: Ian M•
27 июля 2018 г.
Had a lot issues with the quiz grader.
25 мая 2022 г.
Why? A student comes to learn about things. They generally hope, by implication, to be taught by qualified staff who know what they are talking about and who know their courses. These people are not here. The people in the videos are not answering questions. Those who answer questions here don't know much about the course. Students have to give them links, after already telling them section titles. Even when they replied, students would have to try hard to bring them to the course material under discussion - otherwise, they wrote about something else. Furthermore, they are definitely not subject experts who are qualified answer such highly technical subject matters. They can give students links over the Internet, in most cases. Then, why do students come here? Youtube and Internet would be enough. Thirdly, the course is terribly created, scripts mostly read to students by machine-generated voices. Materials were mostly quite randomly put together. Later materials cannot find earlier discusions for foundational discussions. There were many concepts not explained, discussions where prior foundations could not be found. There are labs clearly missing. Introductions only, but no actual labs. Asked the 'staff'. They were beating around the bush and not admitting the obvious. I have gotten so many apologies that I realize they are mostly copied and pasted and sent to me. The small merit this course has is that there are videos and support staff replied, often by scratching the surface. In short, the course is a perfect representation of the greed, greed, and more greed of the training provider. Productivity, profit and scale of operations are their objectives. Not students' education and welfare.
автор: Dhruv D•
29 нояб. 2020 г.
Probably my least favourite in this series. Never really dives into proper pre-processing and feature engineering beyond 1 good lecture by Lak and instead tries to shoehorn Google DataFlow and other services where possible. Worth skipping if you're time constrained
автор: Jakub B•
12 окт. 2020 г.
Huge improvement from previous version, the notebooks actually run and use recent Tensorflow version. Still, some parts are abysmal, like quizzes that don't teach anything other than memorizing some facts from lectures, and labs that have extremely complex examples but do not require any effort from the learner, and don't test any skills.
автор: Sudesh A•
28 июля 2018 г.
The videos are good and better than the last two courses in the specialization; however, the labs lack proper instructions and not that helpful. This course seems like more of an advertisement for Google Cloud Platform than feature engineering: details of engineering part is hardly covered in the course; more emphasis is on demonstrating on how to do it on GCP.
автор: Antony J•
18 нояб. 2020 г.
In depth and advanced. I spent hours poring over the Jupyter notebooks and consequently derived a great deal of value from the course.
автор: Bruno Z•
29 сент. 2021 г.
1. Some of the labs don't work (any more), require old versions of TFX or lack information.
2. Quizzes have blatantly obvious mistakes
The lectures and slides are good, though.
автор: Pablo I F•
22 июля 2020 г.
Very bad subtitles, a lot of errors, so for the non english speakers it becomes hard to follow the videos
автор: Aniket D B•
21 сент. 2020 г.
This course has less focus on feature engineering and more focus on GCP
автор: Batkov I O•
23 июля 2021 г.
wasting of time
автор: Ayush T•
5 сент. 2019 г.
This course and the next course of the specialization is the most important course of the specialization. The reason is that other course except the first course deals with the working of APIs which might change in the near future but the insight that this couse provide on some of the topics is really really important, which I've not seen much discussed. This course is definitely a must-do.
автор: Richard M H J•
26 мая 2020 г.
This course starts to bring together the first three courses to apply TensorFlow. I have been waiting for us to get to this point in the specialization. Perhaps the background of earlier courses helps understand the Google infrastructure to support real TensorFlow problems. Perhaps I'm just impatient. Anyway, this course hit on a lot of topics but it is improving my use of TF.
автор: Sinan G•
6 сент. 2018 г.
The course provides an overview and details of a very varied, comprehensive, and advanced range of possibilities to do feature engineering. Because the software and API's presented have a lot of details you will have to work a lot more with the information provided to attain a "hands-on" feeling. However you get a good starting point and knowledge of the possibilities.
автор: Giovanni S•
2 июня 2018 г.
Great course. A bit more difficult than the other 3, because the topic is more complex. Once finished the course you'll get the big picture. It may take some time to digest all little details, but everything is very well explained in a more than exhaustive way. Teachers are also very engaging and never boring. Highly recommend to anyone interested in the topic!
31 окт. 2018 г.
Various enhancements in demonstrating a practical case in feature engineering, starting from ELT through training, evaluating, and lauching an ML engine, taught with a lot of enthusiasm. Recaps of relevant ideas in statistics, algebra and calculus we learned back in our school days (things that some of us "used to know") kind of helped.
автор: Mario R•
13 янв. 2019 г.
This course should be mandatory for any ML practitioner. It teaches you that ML is not only about throwing whatever you want to (sort of) a model and expect to get reasonable results. It is about getting to know your problem and squeeze the data available.
автор: Jafed E G•
6 июля 2019 г.
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
автор: Iman R•
8 июня 2020 г.
This course give you knowledge about how to optimized your machine learning model based on real world case. This course tell you about few tricks to optimized your ml model and tell you how and when to implement the tricks.
автор: Russell H•
24 сент. 2018 г.
A lot of great material that I have not seen covered other ML courses so far. My only complaint is that there is way too much material for a single week. It felt like it should be spread over two weeks at least.