Chevron Left
Volver a Support Vector Machines with scikit-learn

Opiniones y comentarios de aprendices correspondientes a Support Vector Machines with scikit-learn por parte de Coursera Project Network

4.3
estrellas
301 calificaciones
51 reseña

Acerca del 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....

Principales reseñas

MS
22 de abr. de 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
12 de may. de 2020

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

Filtrar por:

1 - 25 de 51 revisiones para Support Vector Machines with scikit-learn

por Tanish M S

30 de mar. de 2020

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

por Rachana C

28 de mar. de 2020

Need more thorpugh explanation of python libraries and functions.

por K B P

6 de sep. de 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.

por Sarthak P

10 de jun. de 2020

It Okay types experience.

por Satyendra k

29 de may. de 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

13 de may. de 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

23 de abr. de 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

10 de jul. de 2020

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

por Lasal J

23 de dic. de 2020

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

por Abhishek P G

18 de jun. de 2020

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

por RUDRA P D

16 de sep. de 2020

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

por Sebastian J

15 de abr. de 2020

Highly recommended to those who have an understanding of SVMs.

por Ujjwal K

9 de may. de 2020

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

por SHOMNATH D

8 de may. de 2020

I am learning so new things from the topic

por Ashwini M

13 de jun. de 2020

Very good project .. learned a lot

por Arnab S

12 de oct. de 2020

Nicely thaught concepts

por Shantanu b

23 de may. de 2020

intersting and helpfull

por javed a

25 de jun. de 2020

Good for the beginners

por JONNALA S R

5 de may. de 2020

Good Course

por SHIV P S P

27 de jun. de 2020

aewsome

por SUDARSHINI A

31 de may. de 2020

Nothing

por Kamlesh C

26 de jun. de 2020

thanks

por KARUNANIDHI D

26 de jun. de 2020

Good

por p s

22 de jun. de 2020

Nice

por tale p

18 de jun. de 2020

good