What Are Support Vector Machine (SVM) Algorithms?
January 30, 2025
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Instructor: Josh Starmer
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(154 reviews)
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Intermediate level
Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.
(154 reviews)
Recommended experience
Intermediate level
Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.
Import data into, and manipulating a pandas dataframe
Format the data for a support vector machine, including One-Hot Encoding and missing data.
Optimize parameters for the radial basis function and classification
Build, evaluate, draw and interpret a support vector machine
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Only available on desktop
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Import the modules that will do all the work (4 min)
Import the data (3 min)
Missing Data Part 1: Identifying Missing Data (4 min)
Missing Data Part 2: Dealing With Missing Data (5 min)
Format Data Part 1: Split the Data into Dependent and Independent Variables (3 min)
Format the Data Part 2: One-Hot Encoding (11 min)
Format the Data Part 3: Centering and Scaling (2 min)
Build A Preliminary Support Vector Machine (2 min)
Optimize SVM with Cross Validation (2 min)
Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine (10 min)
Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.
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The Coursera Project Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their projects. If you're interested in becoming a project instructor and creating Guided Projects to help millions of learners around the world, please apply today at teach.coursera.org.
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Practice new skills by completing job-related tasks.
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Access the tools and resources you need in a pre-configured cloud workspace.
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154 reviews
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Reviewed on Jul 20, 2020
I am a beginner in this area but I learned a lot in this course.
Reviewed on Oct 17, 2020
Short concise and precise course for learning SVM.
Reviewed on Apr 15, 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.
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