[COURSE] Machine Learning and AI: Support Vector Machines (SVMs) in Python
Discover the Power of Support Vector Machines: Unveiling the Hidden Neural Network
In this comprehensive course we demystify the theoretical complexities of support vector machines (SVMs) and unveil their connection to neural networks. In the era of deep learning dominance, SVMs remain a vital tool, often overshadowed by their more popular counterparts. Did you know that an SVM is, in fact, a neural network, and their structures are nearly identical when visualized?
Overcome the initial intimidation of SVM theory with our methodical, step-by-step approach. We start with the basics, using Logistic Regression as a foundation, ensuring you grasp the fundamentals of machine learning geometry. This course takes you through essential SVM theories, including:
- Linear SVM derivation
- Understanding Hinge loss and its relation to Cross-Entropy loss
- Insight into Quadratic programming and a review of Linear programming
- Slack variables and Lagrangian Duality
- Exploring Kernel SVMs for non-linear applications
- Polynomial, Gaussian, Sigmoid, and String Kernels
- Achieving infinite-dimensional feature expansion
- Projected Gradient Descent and Sequential Minimal Optimization (SMO)
- RBF Networks (Radial Basis Function Neural Networks)
- Support Vector Regression (SVR)
- Multiclass Classification techniques
As a bonus (VIP version only), we examine how to apply the “Kernel Trick” to enhance other machine learning models, transforming weak models into robust ones. Explore applications like:
- Kernel Linear regression (for regression)
- Kernel Logistic regression (for classification)
- Kernel K-means clustering (for clustering)
- Kernel Principal components analysis (PCA) (for dimensionality reduction)
But wait, if you’re more of a hands-on learner, fear not! We dedicate two full sections to practical applications of SVMs. Learn through real-world examples in:
- Image recognition
- Spam detection
- Medical diagnosis
- Regression analysis
For advanced students, challenge yourself with exclusive coding exercises that offer unique implementations not found elsewhere. Don’t miss the chance to master SVMs and elevate your machine learning expertise. Theory and practice converge in this course, ensuring both beginners and seasoned learners find valuable insights and practical knowledge. Enroll now and unlock the potential of Support Vector Machines!
