Summary
This chapter has provided a comprehensive exploration of the various Python libraries, frameworks, and tools essential for active ML. By navigating through the intricacies of popular libraries such as scikit-learn and modAL, we have explored their capabilities and how they can be effectively applied in active ML scenarios. Additionally, this chapter has expanded your toolkit by introducing a range of other active ML tools, each with its own unique features and potential applications.
Whether you are a beginner taking your first steps in active ML or an experienced programmer seeking to refine your skills, this chapter aimed to equip you with a solid foundation in the tools and techniques of active ML. The knowledge gained here is not just theoretical; it is a practical guide to help you master Python packages for enhanced active ML and to familiarize yourself with a broad spectrum of active ML tools. This understanding will enable you to select and apply the most appropriate...
