This course module provides guidelines for preparing data for machine learning model training, including how to identify unreliable data; how to discard and impute data; how to improve labels; how to split data into training, validation and test sets; and how to prevent overfitting and ensure models can generalize using regularization techniques.| Google for Developers
This course module teaches the fundamental concepts and best practices of working with categorical data, including encoding methods such as one-hot encoding and hashing, creating feature crosses, and common pitfalls to look out for.| Google for Developers