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Coursera Project Network による Predict Employee Turnover with scikit-learn の受講者のレビューおよびフィードバック



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....

Predict Employee Turnover with scikit-learn: 1 - 18 / 18 レビュー


Apr 09, 2020

compact course

by lokesh y

May 05, 2020

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

This course did explain it all.

by Sebastian J

Apr 28, 2020

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

by Harshit C

May 26, 2020

Just right for the basics of Machine Learning

by Mayank S

May 02, 2020

Good Course. Learned a lot. Thanks Sir.

by Ketaki K

Apr 21, 2020

The Course was very productive .

by Dr. V Y

Apr 21, 2020

Overall Good Experience

by Akash

May 23, 2020

great learning

by Dr. A S A A

May 06, 2020

لا يوجد تعليق

by abdul r s n

May 19, 2020


by Samridha K

May 07, 2020

Very good content and specific. Very satisfied. However my only complaint is that I hope the creator had explained the interactive decision tree and rf codes such as setting gini, entropy and other min, max values more with reasoning.

by Erwin D

May 13, 2020

Excellent approach to predicting employee turnovers!

by Harsh N

May 04, 2020

Good for Foundation!


May 24, 2020

nice project

by Oscar J L G

May 07, 2020

very good

by Ahsan M R

May 27, 2020

More details about the project and libraries used would be helpful to get a good understanding.

by Mohit V G

May 23, 2020

no answers for querries

by Manoj K

May 29, 2020

This is not a project at all. This should include proper EDA, Feature Engineering, Model interpretation (not just showing the visuals, you have to interpret it). Many more basic check points are missing from a practical Project perspective.