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:
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