In this chapter, we trained a multiclass classifier to recognize traffic signs from the GTSRB database. We discussed the basics of supervised learning, explored the intricacies of feature extraction, and sneaked a peek into DNNs.
Using the approach we took in this chapter, you should be able to formulate real-life problems as machine learning models, use your Python skills to download a sample labeled dataset from the internet, write your featurizing functions that convert images to feature vectors, and use OpenCV for training off-the-shelf machine learning models that help you solve your real-life problems.
Notably, we left out some details along the way, such as attempting to fine-tune the hyperparameters of the learning algorithm (as they were out of the scope of this book). We only looked at accuracy scores and didn't do much feature engineering by trying to combine...
