Part 1:Real Data Issues, Limitations, and Challenges

In this part, you will embark on a comprehensive journey into Machine Learning (ML). You will learn why ML is so powerful. The training process and the need for large-scale annotated data will be explored. You will investigate the main issues with annotating real data and learn why the annotation process is expensive, error-prone, and biased. Following this, you will delve into privacy issues in ML and privacy-preserving ML solutions.

This part has the following chapters:

• Chapter 1, Machine Learning and the Need for Data
• Chapter 2, Annotating Real Data
• Chapter 3, Privacy Issues in Real Data