Back to Support Vector Machines in Python, From Start to Finish
Learner Reviews & Feedback for Support Vector Machines in Python, From Start to Finish by Coursera Project Network
156 ratings
About the Course
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.
Top reviews
GS
Jun 8, 2020
This is a very good course to start with SVM.I now know the basic coding for SVM.Thank You sir.
VD
Jul 20, 2020
I am a beginner in this area but I learned a lot in this course.
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26 - 27 of 27 Reviews for Support Vector Machines in Python, From Start to Finish
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.
By Aakash S
•Jan 2, 2023
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.