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Comentários e feedback de alunos de Predict Employee Turnover with scikit-learn da instituição Coursera Project Network

4.4
estrelas
242 classificações
41 avaliações

Sobre o curso

Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed....

Melhores avaliações

RS
31 de Mai de 2020

I am glad to have taken this course. I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick.

LY
4 de Mai de 2020

I was looking for Elaborated explanation of the project and implement it to clear the concept.\n\nThis course did explain it all.

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1 — 25 de 41 Avaliações para o Predict Employee Turnover with scikit-learn

por UNMILON P

9 de Abr de 2020

compact course

por Lokesh Y

5 de Mai de 2020

I was looking for Elaborated explanation of the project and implement it to clear the concept.

This course did explain it all.

por Arnab S

26 de Set de 2020

A good place to learn the implementation of Random Forest and Decision Trees and how to interpret the results.

por Taesun Y

3 de Jun de 2020

the course was designed well and easy to follow. I was hoping to learn a bit more advanced stuff but picked up some useful libraries that I never used it before. Just watch out for little typo when you named a dataset as "data" and next section of the video you called it "hr". The other thing I noticed that if you re-record the videos without you making mistakes along the way would have been much better for students to follow you and save time. cheers,

por Frank M N

7 de Set de 2020

Really liked it! Up to the point on a useful subject which directly translate into business reality. Within that package you get a very nice and detailed forest of random forest!

por Alina I H

9 de Nov de 2020

Just the perfect course - a well instructed project that helped me exactly with my employee turnover prediction project at work. Thanks from Germany!

por Rahul S

1 de Jun de 2020

I am glad to have taken this course. I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick.

por samuel c j

4 de Jul de 2020

I learn a lot in a small amount of time. I would like to see more advanced projects from you!

por Sebastian J

28 de Abr de 2020

Excellent course for those who knowledge on the topics mentioned in the content.

por Ricardo D

29 de Set de 2020

Great course. It goes to the point about decision trees and random forests.

por Kaushal P

9 de Jun de 2020

very useful project, really enjoyed while doing!

por Harshit C

26 de Mai de 2020

Just right for the basics of Machine Learning

por Mayank S

2 de Mai de 2020

Good Course. Learned a lot. Thanks Sir.

por Ketaki K

21 de Abr de 2020

The Course was very productive .

por Dr. V Y

21 de Abr de 2020

Overall Good Experience

por XAVIER S M

2 de Jun de 2020

Very Helpful !

por Akash

23 de Mai de 2020

great learning

por Dr. A S A A

6 de Mai de 2020

لا يوجد تعليق

por Widhi A P

8 de Jul de 2020

Very Good

por Doss D

14 de Jun de 2020

Thank you

por Kamlesh C

6 de Jul de 2020

THanks

por Vajinepalli s s

18 de Jun de 2020

nice

por tale p

13 de Jun de 2020

good

por SHIV P S P

2 de Jun de 2020

good

por abdul r s n

19 de Mai de 2020

Best