AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Data preprocessing in machine learning refers to the steps taken to clean and organize raw data before training a model. This is a crucial phase in the machine learning pipeline as it significantly impacts the performance of the model. During preprocessing, various tasks are typically carried out, such as removing duplicates, handling missing values, normalizing or standardizing data, and encoding categorical variables. The goal is to convert raw data into a format that is suitable for modeling, ensuring that the model trains effectively on relevant, high-quality data.

While other processes like collecting data and testing for quality do form parts of the broader data handling lifecycle, they do not specifically address the transforming and preparing of data to make it ready for model training, which is the essence of data preprocessing. Hence, the focus on organizing and cleaning raw data distinguishes this concept as a foundational element in developing machine learning systems.