This lesson summarizes the topics we'll be covering in section 33 and why they'll be important to you as a data scientist.

You will be able to:

• Understand and explain what is covered in this section
• Understand and explain why the section will help you to become a data scientist

Support Vector Machines are a very powerful and popular machine learning technique. They're primarily thought of as classifiers, although they can also be used for regression tasks.

In this section we'll start by introducing the underlying concept on which SVM's are based and then we'll run you through the process of building an SVM from scratch before introducing you to the implementation provided by Scikit-learn.

We'll then introduce the kernel trick - a key technique for making it easier to separate seemingly interspersed data to ease classification.

SVM's are popular for a number of machine learning tasks, and the kernel trick can potentially be applied to a number of machine learning techniques, so this section will give you a really good grounding in techniques for building robust, non-linear classifiers.