Machine Learning with Support Vector Machines

Teaching you how to successfully apply machine learning in a classification setting with support vector machines in Python. The course also features practical sections on multiclass extension and cross-validation using sk-learn.

with Elitsa Kaloyanova

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Course Overview

This course is all about Support Vector Machines – one of the most versatile and widely used techniques in supervised learning. They can be applied to both classification and regression tasks and thanks to kernels, fit different data distributions at a reduced computational cost . In this course, you’ll grasp the theory behind support vector machines andhow to implement and optimize a Support Vector Classifier in Python using sk-learn.

15 High Quality Lessons
9 Practical Tasks
1 Hours of Video
Certificate of Achievement

Skills you will gain

machine learningprogrammingpythontheory

What You'll Learn

We’ll discuss the theoretical aspects of SVMs, such as the hard and soft margin problems, and touch upon kernels. In the practical part, you’ll gain valuable hands-on experience in implementing a Support Vector Classifier in Python using sk-learn.

Support vector machines
Soft margin problem
Hard margin problem
Intuition behind kernels
Support vector classifier in sk-learn
Cross-validation with GridsearchCV in Python


“The Machine Learning field offers countless opportunities for in-depth data analysis,reliablepredictions, and valuable insights. Support vector machines in particular, are one of the most powerful methods in machine learning and, as such, are an indispensable skill in the toolbelt of any data scientist and ML engineer.”

Elitsa Kaloyanova
Content Manager at 365 Data Science
Machine Learning with Support Vector Machines

with Elitsa Kaloyanova

Start Course