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Kursteilnehmer-Bewertung und -Feedback für Predict Employee Turnover with scikit-learn von Coursera Project Network

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242 Bewertungen
41 Bewertungen

Über den Kurs

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....

Top-Bewertungen

RS
31. Mai 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. Mai 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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26 - 41 von 41 Bewertungen für Predict Employee Turnover with scikit-learn

von Karan G

8. Juni 2020

Most of the things that were used were not discussed on how to install them. This consumes a lot of time searching them over the internet.

Also, some of the python libraries that were used are deprecated and are not running on our notebooks. This is also not discussed in great detail

von Murtuza B

28. Juni 2020

Get to learn something new. Like I have not used the interactive dashboard when creating the model. Also get to know about some very useful libraries that I was not using before and I used them more often. Thank you so much for your time and efforts creating this.

von Samridha K

7. Mai 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.

von Bhoom S

8. Sep. 2020

Good practical overview of decision tree and random forest model with Python. The interface for code typing is a bit difficult to navigate with some lag time; hence -1 star in the review.

von Aathira S

12. Juni 2020

Doing hands on project on Rhyme was very helpful as we could listen to the instructions and learn and type it ourselves.

von Frank J C I

14. Juni 2020

Interesting project! It could be better if the course explores more theorical fundamentals of the algorithms

von mukthar o

30. Okt. 2020

It really worth the time and I was exposed to new approach to codes and algorithm.

von Erwin D

13. Mai 2020

Excellent approach to predicting employee turnovers!

von Harsh N

4. Mai 2020

Good for Foundation!

von LAKSHAY S

24. Mai 2020

nice project

von Oscar J L G

7. Mai 2020

very good

von Ahsan R

27. Mai 2020

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

von Amlan C

12. Juni 2020

The codeing environment is very bad very much lagging

von Mohit V G

23. Mai 2020

no answers for querries

von Prateek G

6. Sep. 2021

difficult understanding with it

von Manoj K

29. Mai 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.