This session covers critical data preprocessing steps essential for machine learning model performance and accuracy. Trainees will learn techniques for handling missing values, such as imputation and flagging, and encoding categorical and real-valued data into numerical formats. The session will include normalization and standardization to improve gradient-based algorithms’ convergence rates, and data preparation methods like one-hot encoding and representation. Understanding the hypothesis space and managing biases through cross-validation are key topics. The session will also cover partitioning data into training, validation, and test sets, and optimizing model performance through parameter tuning with grid or random search.
Date: 31 July 2024
Given by: Dr. Sarah Alotaibi
Recording Link: https://videolectures.net/AI_Olympiad_2024_alotaibi_data/