Institut Supérieur d'Ingénierie Numérique de Tunis (HIDE)

Data quality is essential for successful machine learning models, but real-world data often contains issues such as missing values, inconsistencies, noise, duplicates, and outliers. Data preprocessing addresses these problems by cleaning, transforming, and preparing raw data before analysis. In this course, students learn techniques such as handling missing data, detecting outliers, normalization, feature scaling, categorical encoding, and feature selection. The course also introduces preprocessing methods for different data types, including numerical, categorical, text, and image data. By the end of the course, students will be able to prepare reliable datasets for machine learning applications.