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學生對 Google 云端平台 提供的 特色工程 的評價和反饋

4.5
1,697 個評分
189 條評論

課程概述

Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow....

熱門審閱

GS

2020年4月8日

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.

OA

2018年11月25日

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.

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1 - 特色工程 的 25 個評論(共 190 個)

創建者 Yasim K

2018年9月12日

The tf.transform and Apache Beam concepts are not explained in simple ways.

Also the lab jumped from simple programs to complex programs.

創建者 Robert U

2019年6月11日

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.

創建者 Stephen R

2018年8月26日

A lot of the code, did not work.

創建者 Mike W

2019年6月22日

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.

創建者 Adrian H

2020年5月10日

A lot of the labs need updating and revising and made more meaningful.

創建者 Martin A K

2019年5月31日

Would appreciate more guidance on the exercises

創建者 Ian M

2018年7月27日

Had a lot issues with the quiz grader.

創建者 lee.simon3

2022年5月25日

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.

創建者 Dhruv D

2020年11月29日

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

創建者 Jakub B

2020年10月12日

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.

創建者 Sudesh A

2018年7月28日

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.

創建者 Antony J

2020年11月18日

In depth and advanced. I spent hours poring over the Jupyter notebooks and consequently derived a great deal of value from the course.

創建者 Bruno Z

2021年9月29日

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.

創建者 Pablo I F

2020年7月22日

Very bad subtitles, a lot of errors, so for the non english speakers it becomes hard to follow the videos

創建者 Aniket D B

2020年9月21日

This course has less focus on feature engineering and more focus on GCP

創建者 Batkov I O

2021年7月23日

wasting of time

創建者 Ayush T

2019年9月5日

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.

創建者 Richard M H J

2020年5月26日

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.

創建者 Sinan G

2018年9月6日

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.

創建者 Giovanni S

2018年6月2日

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!

創建者 丸瀬重雄

2018年10月31日

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.

創建者 Mario R

2019年1月13日

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.

創建者 Jafed E G

2019年7月6日

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

創建者 Iman R

2020年6月8日

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.

創建者 Russell H

2018年9月24日

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.