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Language Classification with Naive Bayes in Python に戻る

Coursera Project Network による Language Classification with Naive Bayes in Python の受講者のレビューおよびフィードバック

4.5
90件の評価
19件のレビュー

コースについて

In this 1-hour long project, you will learn how to clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling, and the impact that class imbalance of training data has on classification performance. You will learn how to use subword units to further mitigate the negative effects of class imbalance, and build an even better model....
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Language Classification with Naive Bayes in Python: 1 - 19 / 19 レビュー

by Lakshit A

May 09, 2020

The project was good enough to understand the concepts of Naive Bayes that too in Python, but the Rhyme virtual machine was just not right place to learn on the go things cause it's seriously slow and video buffers too much. Above all it was an awesome project.

by Yulius D

May 31, 2020

a great explanation from the instructor

by Grace A J P

May 03, 2020

An excellent course, I recommend it.

by Mayank S

Apr 28, 2020

Good Course.

Well Explained

by Ashwin P

May 12, 2020

excellent Naive Bayes

by Hafiz M S H

Jul 01, 2020

The course is good

by XAVIER S M

Jun 02, 2020

Very Helpful !

by FRANSESCO M

Jun 26, 2020

Great Project

by DRISSI B

Jun 15, 2020

Good course

by Doss D

Jul 02, 2020

Thank you

by Swapna V

Jul 02, 2020

good one

by tale p

Jun 26, 2020

good

by p s

Jun 25, 2020

Good

by Rifat R

Jun 13, 2020

Nice

by Veeramanickam M

May 04, 2020

Thank you, required more information on naive Bayes with classification.

by Francisco R P d l R

Jun 24, 2020

Very nice guided project and useful for my job purposes

by Akanksha S

May 18, 2020

good

by Harsh S

Jun 22, 2020

the material and explanation was great, but i was not able to download the project file and also using the cloud virtual machine was not a very smooth experience.

by P. T

Jun 30, 2020

It is a very good experience to do this project but it would be better if it has more explanation.