Support Vector Machines (SVM) Explained | Hyperplanes, Kernel Functions & More!

Описание: Discover the power of Support Vector Machines (SVM) in this comprehensive tutorial designed for beginners! In this video, we break down everything you need to know about SVMs—from the basics of what an SVM is, to how hyperplanes are used for classification. Learn how a separating hyperplane divides data, explore the concept of maximal margin classifiers and support vector classifiers, and understand the role of slack variables in handling misclassifications.

We also dive into how SVMs tackle non-linear boundaries using kernel functions, and wrap up with a practical guide to model evaluation with confusion matrices. This step-by-step explanation is perfect for anyone looking to master SVMs and enhance their machine learning skills.

👇 *In This Video* :

📌What is SVM?
📌Understanding Hyperplanes in SVM
📌Classification using Separating Hyperplanes
📌Maximal Margin Classifier & Support Vector Classifier
📌Slack Variables Explained
📌Handling Non-linear Boundaries with Kernel Functions
📌Model Evaluation and Confusion Matrix

🕒 *Timestamps* :
00:00 - Introduction
00:40 - What is SVM?
01:30 - Understanding Hyperplanes
02:15 - Classification Using Separating Hyperplanes
02:42 - Maximal Margin Classifier(Support Vectors)
03:42 - Slack Variables
04:14 - Handling Non-linear Boundaries with Kernel Functions
05:28 - Model Evaluation & Confusion Matrix

Boost your understanding of machine learning and data science with this easy-to-follow SVM tutorial!

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