Chevron Left
Back to Support Vector Machines with scikit-learn

Learner Reviews & Feedback for Support Vector Machines with scikit-learn by Coursera Project Network

4.3
stars
306 ratings

About the Course

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....

Top reviews

MS

Apr 22, 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.

SY

May 12, 2020

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

Will definitely worth your time.

Filter by:

1 - 25 of 51 Reviews for Support Vector Machines with scikit-learn

By Tanish M S

•

Mar 30, 2020

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

By Rachana C

•

Mar 28, 2020

Need more thorpugh explanation of python libraries and functions.

By Sagar

•

Sep 6, 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.

By Sarthak P

•

Jun 10, 2020

It Okay types experience.

By Satyendra k

•

May 29, 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.

By 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.

By 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.

By ANURAG P

•

Jul 10, 2020

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

By Lasal J

•

Dec 23, 2020

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

By Abhishek P G

•

Jun 18, 2020

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

By RUDRA P D

•

Sep 16, 2020

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

By Sebastian J

•

Apr 15, 2020

Highly recommended to those who have an understanding of SVMs.

By Ujjwal K

•

May 9, 2020

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

By SHOMNATH D

•

May 8, 2020

I am learning so new things from the topic

By Ashwini K M

•

Jun 13, 2020

Very good project .. learned a lot

By Arnab S

•

Oct 12, 2020

Nicely thaught concepts

By Shantanu b

•

May 23, 2020

intersting and helpfull

By javed a

•

Jun 25, 2020

Good for the beginners

By JONNALA S R

•

May 5, 2020

Good Course

By SHIV P S P

•

Jun 27, 2020

aewsome

By SUDARSHINI A

•

May 31, 2020

Nothing

By Kamlesh C

•

Jun 26, 2020

thanks

By KARUNANIDHI D

•

Jun 26, 2020

Good

By p s

•

Jun 22, 2020

Nice

By tale p

•

Jun 18, 2020

good