Chapter 6: Support Vector Machines (SVM) introduces one of the most powerful algorithms in machine learning for classification and regression tasks. SVMs are widely used for high-dimensional data and are particularly effective in tasks where the classes are not easily separable. This chapter provides an in-depth look at the theory behind SVM, how they find the optimal hyperplane, and how to implement them in real-world applications.
What You’ll Learn:
- The fundamentals of Support Vector Machines and their core concepts.
- How SVMs work by finding the optimal hyperplane that separates different classes.
- How to apply SVM for both binary and multi-class classification tasks.
- Techniques for tuning SVM parameters to improve model performance and prevent overfitting.
Why This Chapter?
- Perfect for learners looking to understand and apply advanced classification techniques.
- Includes practical examples and code to help you implement SVM in real-world datasets.
- Learn how to build highly accurate models for complex classification problems using SVM.
