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Kursteilnehmer-Bewertung und -Feedback für Support Vector Machines with scikit-learn von Coursera Project Network

301 Bewertungen
51 Bewertungen

Über den Kurs

In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model. 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....


22. Apr. 2020

Learned about SVM.\n\nNeed t revisit the code and get most out of it.\n\nThings were concise and that is the strength of the course.

12. Mai 2020

This guided project will definitely give you a practical approach to what you have read in SVM.\n\nWill definitely worth your time.

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1 - 25 von 51 Bewertungen für Support Vector Machines with scikit-learn

von Tanish M S

30. März 2020

The instructor has mastery over these topics. I really enjoyed the session!

von Rachana C

28. März 2020

Need more thorpugh explanation of python libraries and functions.

von K B S P

6. Sep. 2020

The explanation could have been better. I didn't understand the reason behind giving less importance to the conceptual topics. Hope to see some good explanation from other projects.

von Sarthak P

10. Juni 2020

It Okay types experience.

von Satyendra k

29. Mai 2020

I am satendra kumar, Ipresuing b. Tech Me lkg ptu main campus kapurthala . I learned about in SVM machine learning, machine learning are three type superwise learning, non superwise learning and re- superwise letaning. SVM likes in the superwise learning. SVM are two types quadrilateral and circle are modle training.

von Shubham Y

13. Mai 2020

This guided project will definitely give you a practical approach to what you have read in SVM.

Will definitely worth your time.

von Mayank S

23. Apr. 2020

Learned about SVM.

Need t revisit the code and get most out of it.

Things were concise and that is the strength of the course.


10. Juli 2020

Application-based course with detailed knowledge of SVMs along with an implementation in image classification

von Lasal J

23. Dez. 2020

Nicely Done, Just wished if we used real-world datasets instead of the sci-kit learn one.

von Abhishek P G

18. Juni 2020

I am grateful to have the chance to participate in an online course like this!


16. Sep. 2020

The course is like a crash course on SVMs with good explanation of concepts.

von Sebastian J

15. Apr. 2020

Highly recommended to those who have an understanding of SVMs.

von Ujjwal K

9. Mai 2020

Nice Project! But theory should have explained a little more.


8. Mai 2020

I am learning so new things from the topic

von Ashwini M

13. Juni 2020

Very good project .. learned a lot

von Arnab S

12. Okt. 2020

Nicely thaught concepts

von Shantanu b

23. Mai 2020

intersting and helpfull

von javed a

25. Juni 2020

Good for the beginners


5. Mai 2020

Good Course

von SHIV P S P

27. Juni 2020



31. Mai 2020


von Kamlesh C

26. Juni 2020



26. Juni 2020


von p s

22. Juni 2020


von tale p

18. Juni 2020