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Вернуться к Support Vector Machine Classification in Python

Отзывы учащихся о курсе Support Vector Machine Classification in Python от партнера Coursera Project Network

4.4
звезд
Оценки: 141
Рецензии: 26

О курсе

In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python. We will walk you step-by-step into Machine Learning supervised problems. With every task in this project, you will expand your knowledge, develop new skills, and broaden your experience in Machine Learning. Particularly, you will build a Support Vector Machine algorithm, and by the end of this project, you will be able to build your own SVM classification model with amazing visualization. In order to be successful in this project, you should just know the basics of Python and classification algorithms....

Лучшие рецензии

AM
2 мая 2020 г.

Straight to the point, take a little bit of time and it is very useful for anyone seeking more knowledge in this domain.\n\nThumbs up to the instructor.

AG
16 июня 2020 г.

I like the way we got involved into practice by setting goals which are a bit challenging yet we want to achieve successfully.

Фильтр по:

1–25 из 26 отзывов о курсе Support Vector Machine Classification in Python

автор: Rohit L

4 мая 2020 г.

Course is completely different from the topic

автор: Ammar M

3 мая 2020 г.

Straight to the point, take a little bit of time and it is very useful for anyone seeking more knowledge in this domain.

Thumbs up to the instructor.

автор: Abhishek P G

17 июня 2020 г.

I like the way we got involved into practice by setting goals which are a bit challenging yet we want to achieve successfully.

автор: OTOOSAKYI D O

30 нояб. 2020 г.

This project is very much educative from start to finish and it enables a beginner to master some key concepts.

автор: Ramya G R

8 июня 2020 г.

I really enjoyed working with this project. Thank you so much for the valuable teaching.

автор: Nikhil K

6 мая 2020 г.

Nice experience to get acquinted with the algorithm

автор: Khandaker M A

4 авг. 2020 г.

Enjoyed a lot with the course. Good enough.

автор: Vishnu N

2 авг. 2020 г.

It is good course to get to know about SVM

автор: Shreyas K

18 июня 2020 г.

Good for beginners.

автор: Gangone R

4 июля 2020 г.

very useful course

автор: JONNALA S R

11 мая 2020 г.

Simple Course

автор: Dr. J V S

27 июля 2020 г.

GOOD COURSE

автор: PAVITHRA B

8 сент. 2020 г.

EXCELLENT

автор: SASI V T

12 июля 2020 г.

EXCELLENT

автор: Doss D

26 июня 2020 г.

Thank you

автор: Vijayalaxmi

3 сент. 2020 г.

Nice

автор: p s

23 июня 2020 г.

Good

автор: Vajinepalli s s

16 июня 2020 г.

nice

автор: MR. J T R A

13 июня 2020 г.

good

автор: Pratirodh K

27 сент. 2020 г.

it was a little bit hard to get message, when he did not write the code for visualizing and copied it. My code never get executed even I copied.

автор: Yasir A

12 сент. 2020 г.

Good Course. But voice of instructor was not clear enough and he was very slow. Overall, I have learned some new things. Thanks Instructor.

автор: Deleted A

19 авг. 2020 г.

Gave me a good intuition for applying SVM classifier in python as well as visualising predictions, thanks for

guiding me through this.

автор: alekhya b

17 мая 2020 г.

It's very good course for beginnes

автор: Pranshu A

2 сент. 2020 г.

Instructor should not use scikitlearn prebuilt module for svm classification project.

автор: Adarsh K

12 мая 2020 г.

Nowhere SVM is used in this project.