306 ratings
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Understand the theory behind support vector machines
Builld SVM models with scikit-learn to classify linear and non-linear data
Determine the strengths and limitations of SVMs
Develop an SVM-based facial recognition model
In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model. 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 Python, Jupyter, and scikit-learn pre-installed. 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.
Data Science
Machine Learning
Python Programming
Support Vector Machine (SVM)
Data Analysis
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Getting Started
Beyond Linear Discriminative Classifiers
Many Possible Separators
Plotting the Margins
Training an SVM Model
Facial Recognition with SVMs
Preprocessing the data set
Hyperparameter Tuning with Grid-Search Cross Validation
Visualize Test Images
Evaluating the Support Vector Classifier
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by DB
May 8, 2020It would have been better if the theory would have been explain in more depth.
by AP
Jul 9, 2020Application-based course with detailed knowledge of SVMs along with an implementation in image classification
by PK
May 15, 2020Beginner friendly and walks you through most of major steps which are usually done in Machine Learning Projects with SVM. Good course
by AJ
Nov 28, 2020nice application. user should know mathematics about SVM, PCA for better experience of this project.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.
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Auditing is not available for Guided Projects.
At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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