Part 3: Technical Approaches to Better Data

In this part, we explore technical approaches to enhance data quality and management in machine learning. We cover topics ranging from data cleaning, programmatic labeling, and synthetic data usage, to addressing bias and handling rare events. Each chapter gives you essential skills and knowledge to work efficiently with data in machine learning, highlighting how important good quality data is in building robust ML systems.

This part has the following chapters:

• Chapter 5, Techniques for Data Cleaning
• Chapter 6, Techniques for Programmatic Labeling in Machine Learning
• Chapter 7, Using Synthetic Data in Data-Centric Machine Learning
• Chapter 8, Techniques for Identifying and Removing Bias
• Chapter 9, Dealing with Edge Cases and Rare Events in Machine Learning