This is the moment you've been waiting for, isn't it?

We have covered all of the bases—we have a functioning Python environment, we have OpenCV installed, and we know how to handle data in Python. Now, it's time to build our first machine learning system! And what better way to start off than to focus on one of the most common and successful types of machine learning: supervised learning?

From the previous chapter, we already know that supervised learning is all about learning regularities in training data by using the labels that come with it so that we can predict the labels of some new, never-seen-before test data. In this chapter, we want to dig a little deeper and learn how to turn our theoretical knowledge into something practical. We will cover classification and regression, along with different evaluation metrics for regression and classification.

In this chapter, we will cover the following topics:

• Exploring the difference between classification and regression and learning when to use them
• Learning about the k-nearest neighbor (k-NN) classifier and implementing it in OpenCV
• Using logistic regression for classification
• Building a linear regression model in OpenCV and learning about how it differs from Lasso and ridge regression

Let's jump right in!