SVM_and_Kernels_presentation_with_code.pptx

SVM with kernel and code

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 Example: Classifying emails as spam or not spam.

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. Mathematical Understanding (Basic Level) Margin = 2 / ||w|| (where w is the weight vector) Goal: Maximize the margin to improve classification.

6. Support Vectors Support vectors are the data points closest to the decision boundary. They are critical in defining the position and orientation of the hyperplane.

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

8. 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.

9. 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.

10. Visualization of Kernels Different kernels project data differently to enable separation.

11. Practical Applications of SVM Applications include: - Face detection - Text classification - Handwriting recognition - Medical diagnosis

12. Advantages and Disadvantages of SVM Advantages: - Effective in high dimensions - Versatile with kernel trick Disadvantages: - High training time for large datasets - Not suitable for overlapping classes

13. 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)

14. 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.

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