A curated list of all (almost) machine learning and deep learning algorithms grouped by category. This repository is meant to help understand the various machine learning algorithms (Inspired by awesome-machine-learning). You can star this repo for future reference :)

Please see CONTRIBUTING for more details on how to contribute.

• Regression Algorithms

  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)

• Instance-Based Algorithms

  • k-Nearest Neighbor (kNN)
  • Self-Organizing Map (SOM)
  • Support Vector Machines (SVM)

• Clustering Algorithms

  • k-Means
  • Expectation Maximisation (EM)
  • Hierarchical Clustering

• Bayesian Algorithms

  • Naive Bayes
  • Gaussian Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Network (BN)
  • Bayesian Belief Network (BBN)

• Decision Tree Algorithms

  • Conditional Decision Trees
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0

• Regularization Algorithms

  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)

• Association Rule Learning Algorithms

  • Apriori algorithm
  • Eclat algorithm

• Ensemble Algorithms

  • Random Forest
  • Boosting
  • Bootstrapped Aggregation (Bagging)
  • AdaBoost
  • Weighted Average (Blending)
  • Stacked Generalization (Stacking)
  • Gradient Boosting Machines (GBM)
  • Gradient Boosted Regression Trees (GBRT)

• Artificial Neural Network Algorithms

  • Perceptron
  • Multilayer Perceptrons (MLP)
  • Back-Propagation
  • Stochastic Gradient Descent
  • Hopfield Network
  • Radial Basis Function Network (RBFN)

• Deep Learning Neural Network Algorithms

  • Convolutional Neural Network (CNN)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
  • Stacked Auto-Encoders
  • Deep Boltzmann Machine (DBM)
  • Deep Belief Networks (DBN)
  • Generative Adversarial Networks (GANs)

• Dimensionality Reduction Algorithms

  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Linear Discriminant Analysis (LDA)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit

• Articles from machine learning Mastery. (Inspired from)
• Articles from towardsdatascience. (Credit goes to the respective authors)
• Articles from medium. (Credit goes to the respective authors)
• Articles from wikipedia.
• Articles from kdnuggets.
• Articles from probabilisticworld.
• Articles from digitalvidya.
• Articles from analyticsvidhya.
• Videos from youtube.
• Articles from opengenus.