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返回到 Support Vector Machines in Python, From Start to Finish

學生對 Coursera Project Network 提供的 Support Vector Machines in Python, From Start to Finish 的評價和反饋

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148 個評分

課程概述

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....

熱門審閱

AH

2020年4月15日

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

2020年6月8日

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

Thank You sir.

篩選依據：

1 - Support Vector Machines in Python, From Start to Finish 的 23 個評論（共 23 個）

創建者 ilay b

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2020年6月6日

the remote desktop is impossible to work with.

just let me work on the jupyter lab...

very low level of course

創建者 Anuganti S

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2020年6月5日

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.

創建者 Muhammet N C

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2020年9月17日

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

創建者 Ali M H

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2020年4月16日

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.

創建者 Mofei W

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2020年11月4日

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!!!

創建者 Gouri S

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2020年6月9日

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

Thank You sir.

創建者 Mayank S

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2020年4月30日

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

創建者 vivek d

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2020年7月21日

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

創建者 Rushikesh S

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2020年8月7日

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

創建者 Vedang B

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2020年10月18日

Short concise and precise course for learning SVM.

創建者 Yasir A

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2020年9月11日

Very helpful. Great instructor.

創建者 Abhimanyu D

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2020年5月9日

nice course

創建者 Doss D

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2020年6月19日

Thank you

創建者 Uppalapati. S S

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2020年6月20日

Great

創建者 Manish N

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2021年5月2日

Good

創建者 p s

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2020年6月25日

Good

創建者 tale p

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2020年6月23日

good

創建者 FRANSESCO M

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2020年6月22日

Best

創建者 Vajinepalli s s

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2020年6月16日

nice

創建者 BHARATH M

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2020年6月7日

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.

創建者 Nilesh A

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2020年5月17日

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.

創建者 Aakash6 S

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2023年1月2日

Could have been more detailed, should have had included more complex data preprocessing and some real life challenges. Also more parameter tuning could have been covered.

創建者 Nikhil T

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2020年7月8日

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

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