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Support Vector Machines for Regression: Machine Learning

Learn to use Support Vector Machines for Regression from a Data Science expert. Code templates included.

Free tutorial

Created by Lucas Bazilio


What you’ll learn

  • Master Support Vector Machines for Regression in Python
  • Become an advanced, confident, and modern data scientist from scratch
  • Become job-ready by understanding how Support Vector Machines really work behind the scenes
  • Apply robust Data Science techniques for Support Vector Machines
  • How to think and work like a data scientist: problem-solving, researching, workflows
  • Get fast and friendly support in the Q&A area


  • No data science experience is necessary to take this course.
  • Any computer and OS will work β€” Windows, macOS or Linux. We will set up your code environment in the course.


You’ve just stumbled upon the most complete, in-depth Support Vector Machines for Regression course online.

Whether you want to:

– build the skills you need to get your first data science job

– move to a more senior software developer position

– become a computer scientist mastering in data science

– or just learn SVM to be able to create your own projects quickly.

…this complete Support Vector Machines for Regression Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Support Vector Machine skills you need to become a data science expert. By the end of the course, you will understand the SVM method extremely well and be able to apply it in your own data science projects and be productive as a computer scientist and developer.

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What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Support Vector Machines for Regression course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the SVM technique. It’s a one-stop shop to learn SVM. If you want to go beyond the core content you can do so at any time.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

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Enroll now using the β€œAdd to Cart” button on the right, and get started on your way to creative, advanced SVM brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Support Vector Machines are waiting!)

Who this course is for:

  • Any people who want to start learning Support Vector Machines in Data Science
  • Anyone interested in Machine Learning
  • Anyone who want to understand how to apply Support Vector Machines in datasets using Python

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Course content

2 sections β€’ 7 lectures β€’ 1h 34m total lengthCollapse all sections

Code Environment Setup1 lecture β€’ 3min

  • Google Colab for Programming in Python02:52

IntroducciΓ³n6 lectures β€’ 1hr 32min

  • Introduction to the Dataset13:48
  • Partition of the Dataset – Time Series Windows18:34
  • Partition of the Dataset – Target Variable05:58
  • Support Vector Machine – Linear Kernel26:55
  • Support Vector Machines – Polynomial Kernels09:12
  • Support Vector Machine – Radial Basis Function (RBF) Kernel17:03

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