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Отзывы учащихся о курсе Проектирование признаков от партнера Google Cloud

4.5
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Оценки: 1,502
Рецензии: 161

О курсе

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 will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models....

Лучшие рецензии

GS

Apr 09, 2020

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

Nov 26, 2018

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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1–25 из 161 отзывов о курсе Проектирование признаков

автор: Robert U

Jun 11, 2019

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: Yasim K

Sep 12, 2018

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

Also the lab jumped from simple programs to complex programs.

автор: Stephen R

Aug 27, 2018

A lot of the code, did not work.

автор: Mike W

Jun 22, 2019

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

May 10, 2020

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

автор: Sudesh A

Jul 28, 2018

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: Martin A K

May 31, 2019

Would appreciate more guidance on the exercises

автор: Ian M

Jul 27, 2018

Had a lot issues with the quiz grader.

автор: Ayush T

Sep 05, 2019

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

May 26, 2020

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

Sep 06, 2018

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

Jun 02, 2018

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!

автор: Shigeo M

Oct 31, 2018

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

Jan 13, 2019

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

Jul 06, 2019

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

Jun 09, 2020

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

Sep 24, 2018

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.

автор: Gowthaman S

Apr 09, 2020

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.

автор: Omar M A

Nov 26, 2018

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: Balaji R

Jan 10, 2020

i really like the effort taken in developing this course, the structure. Kudos to Laks for converting lots of statistical and coding language to very simple understandable english.

автор: Ting-Shuo Y

Sep 23, 2019

Feature engineering is important but less discussed compared to general ML or DNN. Feature cross is a new concept and yet very useful for dealing with large datasets.

автор: Mus A A

Apr 19, 2020

i have decided to join this learn then i am burning for my mind gets handle a process step by step to look after this. Feature Engineering so hard but i can do it.

автор: Patxi G

Mar 07, 2019

The content is great, not just from a technical point of view but for all the know-how that the different instructors share during in the videos and labs.

автор: Carlos V

Jul 01, 2018

Excellent Course and advice from experts about Feature Engineering and data pipelines utilizing advanced processes on GCP, thanks to Google and Coursera.

автор: Enrico A

Oct 27, 2018

This module covers a lot of tricks that should be employed during preprocessing to improve the prediction accuracy of machine learning methods.