ML with Python | Lecture 8 | Support Vector Machine (SVM) | Classification & Hyperplane | Bangla
*📚 ML with Python – Lecture 8 (Bangla)*
Welcome to *Lecture 8* of the *ML with Python* course.
In this lecture, we explore **Support Vector Machine (SVM)**, one of the most powerful supervised machine learning algorithms used for classification and regression. You'll learn how SVM finds the optimal decision boundary, maximizes the margin between classes, and handles both linear and non-linear datasets.
📌 Topics Covered
Introduction to Support Vector Machine (SVM)
Supervised Learning with SVM
Linear vs Non-Linear Classification
Hyperplane and Decision Boundary
Support Vectors
Margin Maximization
Soft Margin vs Hard Margin
Kernel Trick
Linear Kernel
Polynomial Kernel
Radial Basis Function (RBF) Kernel
SVM for Classification
SVM for Regression (SVR) – Overview
Advantages and Limitations of SVM
Real-World Applications of SVM
Python Implementation Overview
📂 Files Covered
ML with Python_SVM.pdf
SVM.zip
MLDS Module 08 – Lecture 08 – Algorithm Details of SVM.mp4
🎯 Learning Outcomes
By the end of this lecture, you will be able to:
Understand how Support Vector Machines classify data.
Learn the concepts of hyperplanes, margins, and support vectors.
Differentiate between linear and non-linear SVM.
Understand the role of kernel functions in solving complex classification problems.
Apply SVM concepts to real-world machine learning tasks.
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