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32 Bewertungen

Über den Kurs

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....

Top-Bewertungen

JS
5. Okt. 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. Juni 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 von 32 Bewertungen für Statistical Data Visualization with Seaborn

von Nagabhairu v k

14. Mai 2020

Not at all useful

von Yaron K

7. Sep. 2021

Shows an example of feature selection using sklearn SelectKBest and RFECV, xgboost plot_importance, and dimensionality reduction using PCA. With seaborn visualizations of EDA and results of running xgboost ML.

The completed notebook is included in the resources, so you can concentrate on learning (rather than on improving your typing skills).

von Suhaimi C

19. Nov. 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.

von José P P D D S

6. Okt. 2020

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

von HAY a

30. Juni 2020

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

von Aakansha S

22. Apr. 2020

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

von Punam P

13. Mai 2020

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

von Jayden P

24. Juni 2021

Clean and simple. No issues with this course .

von SUGUNA M

19. Nov. 2020

Good project based course

von Hitesh J

20. Juli 2020

optimal for beginners

von Doss D

14. Juni 2020

Thank you very much

von Suresh B K

19. Juni 2020

Good experience

von Hector P

13. Sep. 2020

Great project!

von Adolf Y M

11. Okt. 2020

all is good

von Priscila A B

7. Apr. 2021

Perfect!

von amarendra k y

2. Juni 2020

Awesome

von Prakhar M

27. Sep. 2020

Good

von tale p

26. Juni 2020

good

von p s

22. Juni 2020

Good

von Fhareza A

14. Sep. 2020

wow

von Jorge H G G

26. Feb. 2021

I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material. Of course coursera gives me a small discount for having already paid it previously. It is very easy to download the videos and difficult to get hold of the material, but with ingenuity it is possible. Then I recommend uploading them to YouTube and keeping them private for when they want to consult (they avoid legal problems and can share with friends), then they can request a refund.

von Alex K

7. Dez. 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?

von Lilendar R

9. Aug. 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.

von Sebastian A T H

2. Okt. 2020

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

von Gayatree D

3. Juni 2020

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