How to implement SVM (Support Vector Machine) from scratch with Python

TL;DR

Learn how to implement Support Vector Machine (SVM) from scratch using Python and NumPy.

Transcript

welcome to another video of the machine learning from scratch course presented by assembly AI in this series we Implement popular machine learning algorithms using only built-in python functions and numpy in this lecture we learn about support Vector machine or short svm so as always we start with a short Theory section and then we jump to the code... Read More

Key Insights

• 🏛️ SVM seeks a hyperplane with maximum margin to separate classes effectively.
• 🦮 Hinge loss in SVM quantifies the misclassification and guides the training process.
• 🌸 Balancing margin maximization and loss minimization is crucial in SVM optimization.
• 🏋️ Training an SVM model involves iterative weight updates over samples for convergence.
• 🏛️ Predictions in SVM are made by applying a linear function to determine class labels.
• 🤩 SVM implementation from scratch requires defining key components like the fit and predict methods.
• 🏋️ Understanding the gradient descent process is essential for updating weights in SVM training.

Summary & Key Takeaways

• SVM aims to find a linear decision boundary with the largest margin between classes, utilizing a linear model.

• The algorithm involves calculating the hinge loss for training and a cost function that balances margin maximization and loss minimization.

• Training involves initializing weights, applying update rules iteratively, and predicting class labels based on the learned weights.