Understand Regression, Classification, and Unsupervised Learning visually
Start LearningData flows through different learning paradigms based on the problem type
Predict continuous numerical values from input data
Separate data into distinct categories or classes
Find hidden patterns without labeled examples
Regression predicts numerical or yes/no outcomes from input features
A line is fitted through data points to predict continuous values
Predicts probability of binary outcomes using a sigmoid curve
Classification separates data into distinct categories based on features
Splits data through questions to reach classification decisions
Multiple models vote together for higher accuracy
SVM finds the best boundary between data groups with maximum margin
KNN classifies new data points based on the distance to their nearest neighbors
The new point (⭐) connects to its 3 nearest neighbors. With 2 red and 1 blue neighbor, it's classified as Class A.
Neural networks learn complex patterns through layers of connected neurons
Data flows through layers of neurons. Each connection has a weight that's learned during training.
Unsupervised learning finds hidden patterns in data without labeled examples
Data points have no labels - the algorithm must discover structure on its own
Groups similar data points into K clusters
Reduces data dimensionality while preserving variance
These models together power modern Artificial Intelligence
Learn from labeled examples to predict or classify new data
Discover hidden patterns and structures without labels
Powers recommendations, self-driving cars, medical diagnosis & more