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Вернуться к Predict Employee Turnover with scikit-learn

Отзывы учащихся о курсе Predict Employee Turnover with scikit-learn от партнера Coursera Project Network

4.4
звезд
Оценки: 245
Рецензии: 41

О курсе

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....

Лучшие рецензии

FN
6 сент. 2020 г.

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!

RS
31 мая 2020 г.

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

Фильтр по:

26–41 из 41 отзывов о курсе Predict Employee Turnover with scikit-learn

автор: Karan G

8 июня 2020 г.

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

автор: Murtuza B

28 июня 2020 г.

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.

автор: Samridha K

7 мая 2020 г.

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.

автор: Bhoom S

8 сент. 2020 г.

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.

автор: Aathira S

12 июня 2020 г.

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

автор: 20544794508 - I M S

14 июня 2020 г.

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

автор: Mukthar A O

30 окт. 2020 г.

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

автор: Erwin D

13 мая 2020 г.

Excellent approach to predicting employee turnovers!

автор: Harsh N

4 мая 2020 г.

Good for Foundation!

автор: LAKSHAY S

24 мая 2020 г.

nice project

автор: Oscar J L G

7 мая 2020 г.

very good

автор: Ahsan R

27 мая 2020 г.

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

автор: Amlan C

12 июня 2020 г.

The codeing environment is very bad very much lagging

автор: Mohit V G

23 мая 2020 г.

no answers for querries

автор: Prateek G

6 сент. 2021 г.

difficult understanding with it

автор: Manoj K

29 мая 2020 г.

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.