SVM_and_Kernels_with_Visuals_and_Code.pptx

Support Vector Machine

1. Support Vector Machines (SVM) and Kernels - A Beginner’s Guide Presented by: Rushikesh Date: February 2025

2. Introduction to SVM Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. Real-life analogy: Separating fruits based on size and color.

3. Why Use SVM? Advantages of SVM: - Effective in high-dimensional spaces - Works well with clear margin of separation - Robust to overfitting

4. How SVM Works - Intuition SVM finds the optimal hyperplane that best separates the data into classes. Margin: The distance between the hyperplane and the nearest data point from each class.

5. Linear vs. Non-Linear Data Linear data: Can be separated by a straight line. Non-linear data: Requires transformation to a higher dimension.

6. Introduction to Kernels Kernels help SVM handle non-linear data by transforming it into higher dimensions. Analogy: Flattening a crumpled paper to separate points easily.

7. Types of Kernels 1. Linear Kernel: Suitable for linearly separable data. 2. Polynomial Kernel: Handles complex boundaries. 3. RBF Kernel: Maps data into infinite dimensions. 4. Sigmoid Kernel: Similar to neural network activation.

8. Simple Code Snippet Python code using scikit-learn: from sklearn import datasets, svm iris = datasets.load_iris() clf = svm.SVC(kernel='linear') clf.fit(iris.data, iris.target) print('Model trained successfully')

9. Summary and Key Takeaways - SVM is powerful for classification tasks. - Kernels extend SVM to handle non-linear data. - Practical and widely used in real-world applications.

10. Q&A Session Feel free to ask questions!