Congratulations! You have just made a big step toward becoming a machine learning practitioner. Not only are you familiar with a wide variety of fundamental machine learning algorithms, but you also know how to apply them to both supervised and unsupervised learning problems. Moreover, you were introduced to a new and exciting topic, OpenVINO Toolkit. In the previous chapter, we learned how to install OpenVINO and run an interactive face detection and image classification demo, among others. I am sure you have enjoyed learning about those topics.

Before we part ways, I want to give you some final words of advice, point you toward some additional resources, and give you some suggestions on how you can further improve your machine learning and data science skills. In this chapter, we will learn how to approach a machine learning problem and build our own estimator. We will learn how to write our own OpenCV-based classifier in C++ and a scikit-learn-based classifier in Python. 

In this chapter, we will cover the following topics:

• Approaching a machine learning problem
• Writing your own OpenCV-based classifier in C++
• Writing your own scikit-learn-based classifier in Python
• Where to go from here