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Support Vector Machines in Python, From Start to Finish に戻る

Coursera Project Network による Support Vector Machines in Python, From Start to Finish の受講者のレビューおよびフィードバック

4.4
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4件のレビュー

コースについて

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....
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Support Vector Machines in Python, From Start to Finish: 1 - 4 / 4 レビュー

by Ali M H

Apr 16, 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.

by Mayank S

Apr 30, 2020

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

by Abhimanyu

May 09, 2020

nice course

by Nilesh A

May 17, 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.