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Opiniones y comentarios de aprendices correspondientes a Statistical Data Visualization with Seaborn por parte de Coursera Project Network

4.6
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147 calificaciones
29 reseña

Acerca del Curso

Welcome to this project-based course on Statistical Data Visualization with Seaborn. Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results. In this project, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) data set. We will use the results from our exploratory data analysis (EDA) in the previous project, Breast Cancer Diagnosis – Exploratory Data Analysis to: drop correlated features, implement feature selection and feature extraction methods including feature selection with correlation, univariate feature selection, recursive feature elimination, principal component analysis (PCA) and tree based feature selection methods. Lastly, we will build a boosted decision tree classifier with XGBoost to classify tumors as either malignant or benign. 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. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Principales reseñas

JS
5 de oct. de 2020

A machine learning perspective on seaborn capacity, dealing with plots of common results when removing features or selecting important features from dataset

HA
29 de jun. de 2020

Great course for a beginner to be equipped with data science tools and feature selection methods for machine learning!

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1 - 25 de 28 revisiones para Statistical Data Visualization with Seaborn

por NAGABHAIRU V K

14 de may. de 2020

Not at all useful

por Suhaimi W C

18 de nov. de 2020

Awesome guided project. Good overview and interesting subject. I learned a lot using python and seaborn for statistical data visualization. Thanks much for offering this guided project. Highly recommend it to take part 1 first, then this part 2.

por José P P D D S

6 de oct. de 2020

A machine learning perspective on seaborn capacity, dealing with plots of common results when removing features or selecting important features from dataset

por HAY a

29 de jun. de 2020

Great course for a beginner to be equipped with data science tools and feature selection methods for machine learning!

por Aakansha S

22 de abr. de 2020

Thankyou Sir , for explaining in a very simple way it helps me alot!

por Punam P

13 de may. de 2020

Thanks for the course..Nice work and helpful project..

por SUGUNA M

19 de nov. de 2020

Good project based course

por Hitesh J

20 de jul. de 2020

optimal for beginners

por Doss D

14 de jun. de 2020

Thank you very much

por Suresh B K

19 de jun. de 2020

Good experience

por Hector P

13 de sep. de 2020

Great project!

por Adolf Y M

11 de oct. de 2020

all is good

por amarendra k y

2 de jun. de 2020

Awesome

por prakhar m

27 de sep. de 2020

Good

por tale p

26 de jun. de 2020

good

por p s

22 de jun. de 2020

Good

por Fhareza A

14 de sep. de 2020

wow

por Alex K

7 de dic. de 2020

Good instructor, nice bite sized course design and hands on approach. Only thing is the complexity: I probably lack a bit of the theoretical understanding which makes it a little mystifying what is going on, particularly in the second part of the course. At the same time, if I did have the required background I imagine it might be a little basic?

por Lilendar R

9 de ago. de 2020

I think the quizs are very easy, it has to have atleast 10 questions. Beause as we are provided with the jupyter notebook we are understanding everything in detail and expecting some good no of questions in the quiz.

por Sebastian A T H

2 de oct. de 2020

Un excelente curso para profundizar en habilidades prácticas tanto en temas de seaborn como en sklearn

por Gayatree D

3 de jun. de 2020

The course was really nice however, I faced little issues while connecting to the rhyme desktop.

por Bala S

12 de jun. de 2020

The course is really good but i feel it would be even more good if there was more explanation.

por Zahrotul N I

24 de oct. de 2020

Thank you for the lesson but I hope it can be much longer for the explanation.

por Juste N

6 de jul. de 2020

Great project, would have been better with a larger dataset in my opinion.

por Pavithra K

1 de ago. de 2020

project was good but i suggest u to have basic sklearn, ml practice .