Machine Learning
Explore machine learning algorithms through interactive visualizations. Step through gradient descent, watch clusters form, and understand how models learn from data.
Mathematics For Machine Learning
Build a strong mathematical foundation with probability, statistics, and linear algebra
Chance Events
Explore random events, coin flips, and probability basics with interactive visualizations
Expectation
Learn about expected values and probability-weighted outcomes
Variance
Understand variability and standard deviation in probability distributions
Machine Learning Algorithms
Explore supervised and unsupervised learning algorithms with real-time visualizations
Gradient Descent
Interactive optimization visualization on mathematical functions
MinMax Scaler
Scale features to a fixed range for preprocessing
Standard Scaler
Standardize features by removing mean and scaling to unit variance
Linear Regression
Gradient descent optimization for fitting a line to data points
Polynomial Regression
Curve fitting with gradient descent training
Logistic Regression
Binary classification with sigmoid activation
Decision Tree
Classification using recursive partitioning and split criteria
Ensemble Models
Random Forest combining multiple trees for robust predictions
K-Nearest Neighbors
Instance-based classification using distance metrics
K-Means Clustering
Unsupervised learning for grouping similar data
Hierarchical Clustering
Build cluster hierarchies using agglomerative bottom-up merging
Gaussian Mixture Model
Soft clustering with probabilistic assignments via EM algorithm
DBSCAN Clustering
Density-based clustering that finds arbitrarily shaped clusters
Anomaly/Outlier Detection
Detect outliers using Isolation Forest, One-Class SVM, LOF, Z-Score, and IQR methods
Principal Component Analysis
Dimensionality reduction using principal components to capture maximum variance
t-SNE
Nonlinear dimensionality reduction for visualizing high-dimensional data in 2D/3D