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Classifying Cats & Dogs with HOG and SVM

BeginnerGuided Project

Classify images of cats and dogs by extracting Histogram of Oriented Gradients features from them for a Support Vector Machine model! In particular, you will be able to feed an image to the model yourself and get a prediction.

4.7 (13 Reviews)

Language

  • English

Topic

  • Computer Vision

Enrollment Count

  • 181

Skills You Will Learn

  • Machine Learning, Python

Offered By

  • IBM

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • April 30, 2024
About This Guided Project
About
How do we tell apart cats and dogs? As humans, this is an easy task. Dogs don't have claws as sharp as cats. Dogs are social animals whereas cats plot your demise while you sleep prefer their own space. Computers observe these distinctions in a different way. Support Vector Machine is one of the ways that trains a machine to classify an image it sees as a cat or dog. Let's delve right in!

A Look at the Project Ahead
After completing this guided project you will be able to:
  • Extract H.O.G. features from images
  • Train an SVM model on image inputs
  • Tune hyperparameters with Grid Search and evaluate model performance
  • Classify new images of cats and dogs with SVM

What You'll Need
This course mainly uses Python, specifically the OpenCV, sklearn, and numpy libraries. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.

Frequently Asked Questions
> Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
> What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.

Your Instructor
Aije Egwaikhide
Aije is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is currently pursuing her Masters in Management Analytics at Queens University. She is part of the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates.

Cindy Huang
Cindy is a data science associate of the Skills Network team. She has a passion for machine learning to improve user experience, especially in the area of computational linguistics.