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Вернуться к Support Vector Machines with scikit-learn

Отзывы учащихся о курсе Support Vector Machines with scikit-learn от партнера Coursera Project Network

4.3
звезд
Оценки: 280
Рецензии: 46

О курсе

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....

Лучшие рецензии

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.

Фильтр по:

1–25 из 46 отзывов о курсе Support Vector Machines with scikit-learn

автор: Tanish M S

Mar 30, 2020

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

автор: Rachana C

Mar 28, 2020

Need more thorpugh explanation of python libraries and functions.

автор: 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.

автор: 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.

автор: 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.

автор: ANURAG P

Jul 10, 2020

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

автор: Abhishek P G

Jun 18, 2020

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

автор: RUDRA P D

Sep 17, 2020

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

автор: Sebastian J

Apr 15, 2020

Highly recommended to those who have an understanding of SVMs.

автор: Ujjwal K 4 B P E & T I V

May 09, 2020

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

автор: SHOMNATH D

May 08, 2020

I am learning so new things from the topic

автор: Ashwini M

Jun 13, 2020

Very good project .. learned a lot

автор: Shantanu b

May 23, 2020

intersting and helpfull

автор: javed a

Jun 25, 2020

Good for the beginners

автор: JONNALA S R

May 05, 2020

Good Course

автор: SHIV P S P

Jun 27, 2020

aewsome

автор: SUDARSHINI A

May 31, 2020

Nothing

автор: Kamlesh C

Jun 27, 2020

thanks

автор: KARUNANIDHI D

Jun 26, 2020

Good

автор: p s

Jun 22, 2020

Nice

автор: tale p

Jun 18, 2020

good

автор: Vajinepalli s s

Jun 17, 2020

nice

автор: Ankit G

May 28, 2020

nice

автор: Avik C

May 07, 2020

Good

автор: PONDARA K

Jun 01, 2020

5