In this chapter, we learned how to approach a machine learning problem and built our own estimator. We learned how to write our own OpenCV-based classifier in C++ and scikit-learn-based classifier in Python.
In this book, we covered a lot of theory and practice. We discussed a wide variety of fundamental machine learning algorithms, both supervised or unsupervised, and illustrated best practices as well as ways to avoid common pitfalls, and we touched upon a variety of commands and packages for data analysis, machine learning, and visualization.
If you made it this far, you have already made a big step toward machine learning mastery. From here on out, I am confident you will do just fine on your own.
All that's left to say is farewell! I hope you enjoyed the ride; I certainly did.
