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Voltar para Support Vector Machines with scikit-learn

Comentários e feedback de alunos de Support Vector Machines with scikit-learn da instituição Coursera Project Network

4.3
estrelas
266 classificações
44 avaliações

Sobre o curso

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....

Melhores avaliações

MS

Apr 23, 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.

SY

May 13, 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 de 44 Avaliações para o Support Vector Machines with scikit-learn

por Tanish M S

Mar 30, 2020

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

por Rachana C

Mar 28, 2020

Need more thorpugh explanation of python libraries and functions.

por Satyendra k

May 30, 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.

por Shubham Y

May 13, 2020

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

Will definitely worth your time.

por Mayank S

Apr 23, 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.

por ANURAG P

Jul 10, 2020

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

por Abhishek P G

Jun 18, 2020

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

por Sebastian J

Apr 15, 2020

Highly recommended to those who have an understanding of SVMs.

por Ujjwal K

May 09, 2020

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

por SHOMNATH D

May 08, 2020

I am learning so new things from the topic

por ASHWINI K M

Jun 13, 2020

Very good project .. learned a lot

por Shantanu b

May 23, 2020

intersting and helpfull

por javed a

Jun 25, 2020

Good for the beginners

por JONNALA S R

May 05, 2020

Good Course

por SHIV P S P

Jun 27, 2020

aewsome

por SUDARSHINI A

May 31, 2020

Nothing

por Kamlesh C

Jun 27, 2020

thanks

por KARUNANIDHI D

Jun 26, 2020

Good

por p s

Jun 22, 2020

Nice

por tale p

Jun 18, 2020

good

por Vajinepalli s s

Jun 17, 2020

nice

por Ankit G

May 28, 2020

nice

por Avik C

May 07, 2020

Good

por PONDARA K

Jun 01, 2020

5

por MANIKANTA S

May 26, 2020

5