In this chapter, we tried to complement our existing machine learning skills by discussing best practices in model selection and hyperparameter tuning. You learned how to tweak the hyperparameters of a model using grid search and cross-validation in both OpenCV and scikit-learn. We also talked about a wide variety of evaluation metrics and how to chain algorithms into a pipeline.
Now you are almost ready to start working on some real-world problems on your own. But before we part ways, the next chapter will give you some tips and tricks on how to approach a machine learning problem in the wild.
