In this session, we will explore the fundamentals of supervised learning methods in machine learning, including Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Each method will be illustrated with real-world examples to demonstrate their practical applications. Attendees will gain a solid understanding of how these algorithms work, their use cases, and how to implement them effectively in Python. By the end of the session, participants will be equipped with the knowledge to apply these supervised learning techniques to solve various predictive modeling problems.
Date: 8 August 2024
Given by: Dr. Nouf Alshenaifi
Recording Link: https://videolectures.net/AI_Olympiad_2024_nouf_supervised_learning/