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Отзывы учащихся о курсе Language Classification with Naive Bayes in Python от партнера Coursera Project Network

Оценки: 90
Рецензии: 19

О курсе

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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1–19 из 19 отзывов о курсе Language Classification with Naive Bayes in Python

автор: 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.

автор: Yulius D

May 31, 2020

a great explanation from the instructor

автор: Grace A J P

May 03, 2020

An excellent course, I recommend it.

автор: Mayank S

Apr 28, 2020

Good Course.

Well Explained

автор: Ashwin P

May 12, 2020

excellent Naive Bayes

автор: Hafiz M S H

Jul 01, 2020

The course is good

автор: XAVIER S M

Jun 02, 2020

Very Helpful !


Jun 26, 2020

Great Project

автор: DRISSI B

Jun 15, 2020

Good course

автор: Doss D

Jul 02, 2020

Thank you

автор: Swapna V

Jul 02, 2020

good one

автор: tale p

Jun 26, 2020


автор: p s

Jun 25, 2020


автор: Rifat R

Jun 13, 2020


автор: Veeramanickam M

May 04, 2020

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

автор: Francisco R P d l R

Jun 24, 2020

Very nice guided project and useful for my job purposes

автор: Akanksha S

May 18, 2020


автор: 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.

автор: 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.