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Avis et commentaires pour d'étudiants pour Support Vector Machines in Python, From Start to Finish par Coursera Project Network

4.6
étoiles
128 évaluations
21 avis

À propos du cours

In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease. 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 (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with programming in Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices. 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....

Meilleurs avis

AH
15 avr. 2020

It was amazing lecture and teach special with SVM in Python I did learn a lot from him via his tasked. I will download his videos all each tasked have a part of explanation.

GS
8 juin 2020

This is a very good course to start with SVM.I now know the basic coding for SVM.\n\nThank You sir.

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1 - 21 sur 21 Avis pour Support Vector Machines in Python, From Start to Finish

par ilay

6 juin 2020

the remote desktop is impossible to work with.

just let me work on the jupyter lab...

very low level of course

par Anuganti S

5 juin 2020

Nice explanation. Each and every step explained well and in notebook written good explanation.

Thanks for the Project explanation, practice and skill test.

skill test questions is very useful and to gain knowledge on SVM.

par Ali M H

16 avr. 2020

It was amazing lecture and teach special with SVM in Python I did learn a lot from him via his tasked. I will download his videos all each tasked have a part of explanation.

par Mofei W

4 nov. 2020

Best instructor I've ever had. I'm a huge fan of all of your stats videos! Awesome awesome work and I'm really looking forward to more in ML!!!

par Gouri S

9 juin 2020

This is a very good course to start with SVM.I now know the basic coding for SVM.

Thank You sir.

par Mayank S

30 avr. 2020

Great Course. Designed nicely, easy to understand. Now i know how to use SVM.

par Muhammet N C

17 sept. 2020

Short and understandable. Plus, Josh Starmer is a great instructor.

par vivek d

21 juil. 2020

I am a beginner in this area but I learned a lot in this course.

par Rushikesh S

7 août 2020

Excellent Teaching. Makes it easier for you to understand SVM.

par Vedang B

18 oct. 2020

Short concise and precise course for learning SVM.

par MD. Y A

11 sept. 2020

Very helpful. Great instructor.

par Abhimanyu D

9 mai 2020

nice course

par Doss D

19 juin 2020

Thank you

par Uppalapati. S S

20 juin 2020

Great

par p s

25 juin 2020

Good

par tale p

23 juin 2020

good

par FRANSESCO M

22 juin 2020

Best

par Vajinepalli s s

16 juin 2020

nice

par BHARATH M

7 juin 2020

Although there are many lectures on SVM, I have opted for this because of the name " Josh Starmer" BAMM..!! I am a great follower of his youtube videos and I like the way he explains things in easy and understandable way. I hope I have learnt many things to mess around with Support vector Machines. This even helps me in my class project.

par Nilesh A

17 mai 2020

The course really picks up nice on reading, formatting, handling missing values but it's stretched too much and the re-reading of the jupyter notebook seemed too much for me. In the end, I do understand only a bit of SVM's implementation and optimization but not really the concept of SVM.

par Nikhil T

8 juil. 2020

Initially it was explained but after some point he just started reading the code