Machine Learning With Graphs | How Machine Learning Uses Graphs | GNN For Beginners | Simplilearn

🔥Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=WDT52cWmhsI&utm_medium=DescriptionFirstFold&utm_source=Youtube ️🔥 Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=WDT52cWmhsI&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=WDT52cWmhsI&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=WDT52cWmhsI&utm_medium=DescriptionFirstFold&utm_source=Youtube In this video, we’ll take a deep dive into the world of graphs in machine learning. We’ll start by breaking down what graphs are and explore their key components, including nodes, edges, and how they help represent relationships between data points. Then will see why graphs are essential in capturing complex relationships and structures that are often overlooked in traditional data representations. Next, we’ll look at the practical applications of graphs in machine learning . We’ll then discuss how to choose the right graph representation, such as adjacency matrices, adjacency lists, and edge lists, to best suit your data and task. We’ll dive into node embeddings and examine powerful techniques which allow us to learn meaningful features from graph-structured data. Finally, we’ll explore key graph algorithms such as PageRank, graph traversal methods, and graph-based clustering, and how these can be used to extract valuable insights from large-scale networks. 00:00:04 - Introduction 00:03:35 - What is a Graph? 00:05:36 - Why this is Useful? 00:07:31 - Real World Applications 00:10:16 - Machine Learning with Graphs 00:20:35 - Adjacency Matrix 00:23:40 - Adjacen