Chevron Left
Вернуться к Проектирование признаков

Отзывы учащихся о курсе Проектирование признаков от партнера Google Cloud

4.5
Оценки: 1,182
Рецензии: 114

О курсе

Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models. In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future data science projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...

Лучшие рецензии

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.

TY

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.

Фильтр по:

51–75 из 114 отзывов о курсе Проектирование признаков

автор: Sujeethan V

Apr 02, 2019

Great, Labs were redundant at times.

автор: Stephen Y

Apr 06, 2019

Great teaching

автор: Nayanajith P

May 28, 2019

It's nice

автор: Gary T

May 30, 2019

Every aspiring Data Scientist or data analyst MUST take this certification!!

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: Nhan T N

Jul 20, 2019

THe course is okay. The lab is weak in stimulating learner in practice.

автор: Wayne R

Aug 07, 2019

This course definitely shows the value of the GC and TF tools and provided insight into building ML systems

автор: Sri B

Aug 15, 2019

Being a beginner I felt very difficult to understand concepts taught in this course.But the content was awesome

автор: Fathima j

May 09, 2019

great course and very intrsting

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: 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: Anupam S

Dec 02, 2019

Detailed explanations and examples. At places the explanations and intertwined topics do get convoluted, but overall good.

автор: Shivam K

Oct 14, 2019

Labs were good and required effort to complete!

автор: Joel M

Dec 06, 2018

good clear instructions, and valuable content.

автор: Attila B

Dec 08, 2018

Really comprehensive course.Was a bit tough to follow sometimes,but guess it's just beginners problem.

автор: Rohit K A

Dec 24, 2018

No course material for reference

автор: Nagireddy S R

Dec 13, 2018

Felt like it was cut short at the end. Would like to see a bit more on the tf.transform

автор: Gregory R G J

Jan 30, 2019

Thumbs sideways.

I learned a ton but it appears as technology grows and changes updates to the platform is sort of static.

автор: Marcos H

Nov 08, 2018

Very practical and Lak is a great teacher and communicator!

автор: Zezhou J

Nov 09, 2018

The content is quite rich in this course. I feel decomposing it into two weeks might make it structurally more clear.

автор: Joe L

Nov 12, 2018

Great topics, the instructions are great. The only suggestion is cut down the number of different videos. just combine them together. 15 3 mins is not a great experience vs 3 15 min videos.

автор: Harm t M

Mar 13, 2019

This was hard. Not directly applicable to where I am in my machine learning career, but good to know in the future, nonetheless...

автор: Alexander Z

Dec 29, 2018

great content and cool notebooks ... sometimes hard to follow

автор: Said A

Mar 22, 2019

A separate course to emphasise the role and importance of feature engineering in machine learning is what really got to me. With examples and explanation how your model can improve with feature engineering did the trick. Before, it was just a note in my notebook. Now, I really understand the importance of it.

However, having said that, the course could have been much shorter. It feels like, these courses are Google way of promoting its ML Cloud services.

автор: Sandeep K

Jul 30, 2018

this was really good, except removed one start for trifacta integration of dataflow lab.