In this course, discover the stages for training the AI model, such as testing, preprocessing, and selection. Explore techniques for evaluating data quality, focusing on completeness, accuracy, and consistency, such as data profiling, cleansing, and validation. Examine methods for identifying and addressing data bias in AI systems, using statistical methods and visualization tools. Finally, learn about the importance of data integrity and mitigation strategies such as data augmentation and reweighting.
Everyone