In this chapter, we learned the basics of the machine learning model and how to apply a small sample application to understand all the basic tips required to create our own ML application.
Machine learning is complex and involves different techniques for each use case (supervised learning, unsupervised, clustering, and so on), and we learned how to create the most typical ML application and the supervised learning with an SVM.
The most important concepts in supervised machine learning are: first, we need to have an appropriate number of samples or datasets; and second, we need to correctly choose the features that describe our objects correctly. For more information on image features, refer to Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations. Third, choose the best model that gives us the best predictions.
If we don't reach the correct predictions we have to check each one of these concepts to look for where the issue is.
In the next chapter, we will introduce...