Explore machine learning concepts through interactive visualizations and hands-on examples. Learn by doing!
Explore powerful techniques that transform data into higher-dimensional spaces to make complex problems linear and solvable.
Transform data to higher dimensions for linear separation
Support Vector Machines with kernel functions
RBF, Polynomial, Linear, and other kernel functions
Learn how KNN works by finding the most similar data points. Explore classification and regression with interactive examples.
Classify data points based on their nearest neighbors
Predict continuous values using nearest neighbor averaging
Explore Euclidean, Manhattan, and other distance measures
Master the algorithms that find optimal solutions. Learn how machines learn by minimizing errors and maximizing performance.
Watch how we find optimal weights to minimize errors
Optimize with noisy gradients for faster training
Adaptive learning rates with momentum
Understand different regression techniques for predicting continuous values. From simple linear to advanced kernelized methods.
House price prediction using kernel functions
Fit a straight line through data points
Fit curved lines using polynomial functions
Learn how to categorize data into different classes. Explore decision boundaries and classification algorithms.
Tree-based classification with interactive splits
Find optimal decision boundaries with margins
Probabilistic classification with sigmoid function
Compare Naive Bayes and Logistic Regression approaches
Discover patterns in unlabeled data by grouping similar points together. Explore different clustering algorithms and their behavior.
Group data points around centroids
Build clusters in a tree-like structure
Density-based clustering for irregular shapes
Explore the power of artificial neural networks. Watch them learn and understand how they process information.
Watch weights update in real-time
Explore ReLU, Sigmoid, and Tanh functions
See how gradients flow through the network
Learn how combining multiple models can improve predictions. Explore voting, bagging, and boosting techniques.
Multiple decision trees working together
Sequentially improve weak learners
Combine predictions from multiple models