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学生对 Coursera Project Network 提供的 Medical Diagnosis using Support Vector Machines 的评价和反馈

59 个评分
15 条评论


In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning. Note: 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....



1 - Medical Diagnosis using Support Vector Machines 的 15 个评论(共 15 个)

创建者 Vishnu R

Jul 11, 2020

This is not a real world data. Instructor is showing a very basic example. I guess he could have done a real world problem which is little challenging and useful to participants.

创建者 Yasir A

Sep 13, 2020

Nice course.

创建者 Nikita H

Sep 22, 2020

Good course


Jul 11, 2020

A short duration course but with deep and effective learnings. This will give you some insights regarding the power of SVMs

创建者 Diana C

Nov 22, 2020

Just the right amount of explanation and content.


Sep 16, 2021

good and useful

创建者 Gregory G J

Jan 7, 2021

Thumbs Up!

创建者 Kamlesh C

Aug 27, 2020

Thank you


Jul 20, 2020


创建者 Isaac S

Jul 8, 2020


创建者 Edward N

Sep 25, 2021


创建者 Himanka K

Mar 12, 2022

Showed good use of the SVM classifier on real medical diabetes data. However data engineering in the sense of building the dataset used by the model from raw data is missing, which is one of the most important part. Tons of online free example videos are there in youtube on how to apply SVM on real dataset, however for such paid project something extra was needed, which in my case was the data engieering.

创建者 Ran B R

Jun 9, 2021

Quick and basic intro to SVM training. Clearly explained each step and pointed out some issues to avoid. I'd have liked a little explanation of *how* SVMs work (even just how predictions are made once model is trained), but it being "beyond the scope of the project" is not unreasonable

创建者 Rushikesh S

Jul 12, 2020

Good course for practicing SVM Classifiers

创建者 Shubhra P

Jul 23, 2020

A very simple example