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学生对 Coursera Project Network 提供的 Support Vector Machines with scikit-learn 的评价和反馈

303 个评分
51 条评论


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....



Apr 22, 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.


May 12, 2020

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


1 - Support Vector Machines with scikit-learn 的 25 个评论(共 51 个)

创建者 Tanish M S

Mar 30, 2020

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

创建者 Rachana C

Mar 28, 2020

Need more thorpugh explanation of python libraries and functions.

创建者 Bidyasagar

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.

创建者 Sarthak P

Jun 10, 2020

It Okay types experience.

创建者 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.

创建者 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.

创建者 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.


Jul 10, 2020

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

创建者 Lasal J

Dec 23, 2020

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

创建者 Abhishek P G

Jun 18, 2020

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


Sep 16, 2020

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

创建者 Sebastian J

Apr 15, 2020

Highly recommended to those who have an understanding of SVMs.

创建者 Ujjwal K

May 9, 2020

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


May 8, 2020

I am learning so new things from the topic

创建者 Ashwini M

Jun 13, 2020

Very good project .. learned a lot

创建者 Arnab S

Oct 12, 2020

Nicely thaught concepts

创建者 Shantanu b

May 23, 2020

intersting and helpfull

创建者 javed a

Jun 25, 2020

Good for the beginners


May 5, 2020

Good Course

创建者 SHIV P S P

Jun 27, 2020



May 31, 2020


创建者 Kamlesh C

Jun 26, 2020



Jun 26, 2020


创建者 p s

Jun 22, 2020


创建者 tale p

Jun 18, 2020