This brings us to the end of our chapter on machine learning with OpenCV. We started the chapter by introducing the learning paradigm of solving problems. Under such a scheme, we saw that if our algorithm is presented with a lot of data, it can learn to detect patterns and develop its own set of rules that the further help to make predictions on new, unseen data.
We touched upon a lot of different aspects of ML, both in the supervised and the unsupervised domain. We discussed in detail about the k-means clustering algorithm (unsupervised), k-nearest neighbors classifier, and support vector machines (both supervised). We also looked at some practical issues that crop up when we are trying to deploy a machine learning algorithm on our data. Also, you must have noticed that employing ML algorithms enables our programs to make much more human-like predictions using the available data.
This completes our journey that we began in Chapter 1, Laying the Foundation. The book started with the...
