Machine Learning Algorithms Repository

This repository is a comprehensive collection of all known machine learning algorithms. Each algorithm is accompanied by a detailed explanation of how it works, making it a valuable resource for both beginners and experienced practitioners in the field of machine learning.

Contents

The repository is organized into different categories based on the type of machine learning algorithm.

ML algorithm progress tracker

Below are the categories of machine learning that I want to implement and the algoirthms

• Regression algorithms
  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)

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• Instance-based Algorithms
  • k-Nearest Neighbor (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
  • Support Vector Machines (SVM)

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

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• Decision Tree Algorithms
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees

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

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

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• Association Rule Learning Algorithms
  • Apriori algorithm
  • Eclat algorithm

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

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• Deep Learning 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)

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• Dimensionality Reduction Algorithms
  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Linear Discriminant Analysis (LDA)
  • Mixture Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)
  • t-distributed Stochastic Neighbor Embedding (t-SNE)
  • Uniform Manifold Approximation and Projection for Dimension Reduction - [ ] (UMAP)

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