Technical requirements

For this chapter, the technical prerequisites are designed to equip you with the foundational knowledge required to navigate the complexities of this emerging field effectively. They are as follows:

• A solid understanding of the core mechanics of ML is essential, encompassing the processes of training, testing, and evaluating models to ensure that they perform accurately and efficiently. This includes grasping the nuances of how data is split, how models are trained on one subset and tested on another, and how their performance is assessed through various metrics.
• A deep comprehension of optimization in ML is crucial. This involves understanding the techniques and algorithms used to improve model accuracy and efficiency, such as gradient descent and its variants, which play a pivotal role in tuning models to achieve the best possible outcomes.
• A basic familiarity with cybersecurity principles is necessary to understand the threats and vulnerabilities...