Data Preparation for ML/DL Model

The expert session on “Data Preparation for ML/DL Models” aimed to provide participants with essential skills for effective data handling in machine learning and deep learning projects. It began by highlighting the importance of high-quality data and the challenges posed by different data types—structured, unstructured, and semi-structured.

Key topics included data cleaning techniques for addressing missing values, duplicates, and outliers, ensuring data integrity for reliable model predictions. The session also covered data transformation methods such as normalization, standardization, and encoding categorical variables, along with the significance of feature engineering to enhance model performance.

By the end of the session, attendees gained a clear understanding of the critical role of data preparation and practical strategies to improve their ML/DL projects.