Machine Learning
Algorithms that learn patterns from data.
Machine learning algorithms fit models to examples so they can predict, classify, cluster, or transform new data.
Linear Regression
Fits a linear model that predicts a numeric target from input features.
Logistic Regression
Models class probability by applying a logistic function to a linear score.
K-Means
Clusters points by alternating between assigning points to nearest centroids and updating those centroids.
Decision Tree
Predicts by recursively splitting examples with feature tests into simpler subgroups.
Random Forest
Builds an ensemble of decision trees trained on random samples and feature subsets.
Backpropagation
Computes neural-network gradients by applying the chain rule backward through layers.
DBSCAN
Clusters points by dense neighborhoods and labels sparse points as noise.
K-Nearest Neighbors
Predicts from the labels or values of the closest training examples.
Naive Bayes
Classifies examples using feature probabilities and a simplifying independence assumption.
Principal Component Analysis
Projects data onto orthogonal directions that capture the largest variance.
Support Vector Machine
Learns a separating decision boundary by maximizing the margin around support vectors.
Ridge Regression
Fits linear regression with an L2 penalty that shrinks coefficients.
Lasso Regression
Fits a linear model while encouraging some weights to become exactly zero.
Elastic Net
Combines L1 and L2 penalties for sparse but more stable linear models.
Gaussian Mixture Model
Models data as a weighted blend of several Gaussian distributions.
t-SNE
Embeds high-dimensional points into two or three dimensions while preserving local neighborhoods.
Perceptron
Learns a linear binary classifier by updating weights after mistakes.
Multilayer Perceptron
Uses stacked fully connected layers and nonlinear activations to learn flexible functions.
Transformer Attention
Lets each token combine information from other tokens using learned query, key, and value vectors.
Q-Learning
Learns action values from rewards without needing a model of the environment.
Softmax Classifier
Classifies examples by turning linear class scores into probabilities.
Cross-Entropy Loss
Measures how well predicted probabilities match the true class distribution.
Convolutional Neural Network
Learns local filters that detect patterns across grid-like data such as images.
Recurrent Neural Network
Processes sequences by carrying hidden state from one step to the next.
Long Short-Term Memory
Uses gates and a memory cell to model longer sequence dependencies.
GRU
Models sequences with gated hidden-state updates using fewer gates than LSTM.
Autoencoder
Learns a compact representation by reconstructing its input.
Variational Autoencoder
Learns a probabilistic latent space that can reconstruct and generate examples.
Gradient Boosting
Builds an ensemble by adding weak models that reduce current prediction error.
AdaBoost
Builds a classifier by reweighting examples that previous weak learners misclassified.
XGBoost-Style Gradient Boosted Trees
Trains regularized boosted decision trees using gradient and curvature information.
Apriori Algorithm
Finds frequent itemsets by expanding only candidates whose subsets are frequent.
Hidden Markov Model
Models sequences with hidden states that emit observed values over time.
UMAP
Embeds high-dimensional data into a lower-dimensional layout that preserves local neighborhoods.
Gaussian Naive Bayes
Gaussian Naive Bayes learns a predictive, clustering, representation, or policy model from data.
Multinomial Naive Bayes
Multinomial Naive Bayes learns a predictive, clustering, representation, or policy model from data.
Extra Trees
Extra Trees learns a predictive, clustering, representation, or policy model from data.
Isolation Forest
Isolation Forest learns a predictive, clustering, representation, or policy model from data.
One-Class SVM
One-Class SVM learns a predictive, clustering, representation, or policy model from data.
Self-Organizing Map
Self-Organizing Map learns a predictive, clustering, representation, or policy model from data.
Learning Vector Quantization
Learning Vector Quantization learns a predictive, clustering, representation, or policy model from data.
Label Propagation
Label Propagation learns a predictive, clustering, representation, or policy model from data.
Policy Gradient
Policy Gradient learns a predictive, clustering, representation, or policy model from data.
Actor-Critic Method
Actor-Critic Method learns a predictive, clustering, representation, or policy model from data.
Gaussian Process Regression
Predicts a continuous target with a Gaussian process posterior over functions.
Gaussian Process Classification
Gaussian Process Classification addresses bayesian classification by using a Gaussian process latent function with a classification likelihood.
Matrix Factorization
Approximates a data matrix as a product of lower-dimensional latent factor matrices.
Word2Vec Skip-Gram
Learns word embeddings by predicting nearby context words from a center word.