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Support Vector Machines with scikit-learn
  • Level Beginner
  • Duration 3 hours
  • Offered by

About

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.

Auto Summary

Unlock the power of support vector machines (SVMs) with the "Support Vector Machines with scikit-learn" course, designed for aspiring data scientists and AI enthusiasts. This beginner-level course provides an in-depth understanding of SVMs, a robust class of supervised linear models, and equips you with the skills to implement them using Python and scikit-learn. Led by Coursera, this hands-on project-based course runs on the Rhyme platform, offering a seamless learning experience directly in your browser. You'll gain instant access to pre-configured cloud desktops loaded with all necessary software and data, including Python, Jupyter, and scikit-learn, ensuring you can dive straight into your learning journey without any setup hassles. Throughout the 3-hour course, you'll engage in practical tasks such as building a simple facial recognition model, allowing you to apply your newfound knowledge to real-world classification problems. While the course content is accessible globally, it is optimized for learners in North America, with efforts underway to extend the same experience to other regions. Best of all, this comprehensive introduction to SVMs is available for free, making it an excellent starting point for beginners looking to expand their data science and AI skillset. Join now and take the first step towards mastering support vector machines!