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Avis et commentaires pour d'étudiants pour Support Vector Machines with scikit-learn par Coursera Project Network

304 évaluations
51 avis

À propos du cours

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....

Meilleurs avis


22 avr. 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 sur 51 Avis pour Support Vector Machines with scikit-learn

par Tanish M S

30 mars 2020

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

par Rachana C

28 mars 2020

Need more thorpugh explanation of python libraries and functions.

par Bidyasagar

6 sept. 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.

par Sarthak P

10 juin 2020

It Okay types experience.

par 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.

par 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.

par Mayank S

23 avr. 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 juil. 2020

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

par Lasal J

23 déc. 2020

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

par Abhishek P G

18 juin 2020

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


16 sept. 2020

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

par Sebastian J

15 avr. 2020

Highly recommended to those who have an understanding of SVMs.

par 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

par Ashwini M

13 juin 2020

Very good project .. learned a lot

par Arnab S

12 oct. 2020

Nicely thaught concepts

par Shantanu b

23 mai 2020

intersting and helpfull

par javed a

25 juin 2020

Good for the beginners


5 mai 2020

Good Course

par SHIV P S P

27 juin 2020



31 mai 2020


par Kamlesh C

26 juin 2020



26 juin 2020


par p s

22 juin 2020


par tale p

18 juin 2020