Introduction to
Support Vector
Machine (SVM)
Support Vector Machine (SVM) is a powerful supervised learning
algorithm used for both classification and regression tasks.
It finds the optimal hyperplane that separates different classes with the
maximum margin, making it highly effective at handling complex, high-
dimensional data.
However, primarily, it is used for Classification problems in Machine
Learning.
, Fundamentals of SVM and
types of svm
• SVM is a supervised learning algorithm that can be used for both
classification and regression tasks.
• The fundamental idea behind SVM is to find the optimal hyperplane
that maximizes the margin between different classes, making it
robust to outliers and noise.
• There are different types of SVM models, such as linear SVM, non-
linear SVM.
Support Vector
Machine (SVM)
Support Vector Machine (SVM) is a powerful supervised learning
algorithm used for both classification and regression tasks.
It finds the optimal hyperplane that separates different classes with the
maximum margin, making it highly effective at handling complex, high-
dimensional data.
However, primarily, it is used for Classification problems in Machine
Learning.
, Fundamentals of SVM and
types of svm
• SVM is a supervised learning algorithm that can be used for both
classification and regression tasks.
• The fundamental idea behind SVM is to find the optimal hyperplane
that maximizes the margin between different classes, making it
robust to outliers and noise.
• There are different types of SVM models, such as linear SVM, non-
linear SVM.
