# Machine Learning Algorithms 🚀 ![Maintenance](https://img.shields.io/maintenance/yes/2020) ![GitHub issues](https://img.shields.io/github/issues-raw/Sahith02/machine-learning-algorithms) 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 :) ## Contributing Please see [CONTRIBUTING](./CONTRIBUTING.md) for more details on how to contribute. ## List of Algorithms - Regression Algorithms - [Linear Regression](https://towardsdatascience.com/linear-regression-using-least-squares-a4c3456e8570) - [Logistic Regression](https://medium.com/data-science-group-iitr/logistic-regression-simplified-9b4efe801389) - [Stepwise Regression](https://en.wikipedia.org/wiki/Stepwise_regression) - [Multivariate Adaptive Regression Splines (MARS)](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline) - [Locally Estimated Scatterplot Smoothing (LOESS)](https://towardsdatascience.com/loess-373d43b03564) - Instance-Based Algorithms - [k-Nearest Neighbor (kNN)](https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761) - [Self-Organizing Map (SOM)](https://towardsdatascience.com/self-organizing-maps-ff5853a118d4) - [Support Vector Machines (SVM)](https://towardsdatascience.com/support-vector-machine-simply-explained-fee28eba5496) - Clustering Algorithms - [k-Means](https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1) - [Expectation Maximisation (EM)](https://medium.com/@chloebee/the-em-algorithm-explained-52182dbb19d9) - [Hierarchical Clustering](https://www.kdnuggets.com/2019/09/hierarchical-clustering.html) - Bayesian Algorithms - [Naive Bayes](https://towardsdatascience.com/naive-bayes-explained-9d2b96f4a9c0) - [Gaussian Naive Bayes](https://medium.com/@LSchultebraucks/gaussian-naive-bayes-19156306079b) - [Averaged One-Dependence Estimators (AODE)](https://en.wikipedia.org/wiki/Averaged_one-dependence_estimators) - [Bayesian Network (BN)](https://towardsdatascience.com/basics-of-bayesian-network-79435e11ae7b) - [Bayesian Belief Network (BBN)](https://www.probabilisticworld.com/bayesian-belief-networks-part-1/) - Decision Tree Algorithms - [Conditional Decision Trees](https://medium.com/greyatom/decision-trees-a-simple-way-to-visualize-a-decision-dc506a403aeb) - [Classification and Regression Tree (CART)](https://www.digitalvidya.com/blog/classification-and-regression-trees/) - [Iterative Dichotomiser 3 (ID3)](https://towardsdatascience.com/decision-trees-introduction-id3-8447fd5213e9) - [C4.5 and C5.0](https://towardsdatascience.com/what-is-the-c4-5-algorithm-and-how-does-it-work-2b971a9e7db0) - Regularization Algorithms - [Ridge Regression](https://towardsdatascience.com/ridge-regression-for-better-usage-2f19b3a202db) - [Least Absolute Shrinkage and Selection Operator (LASSO)](https://medium.com/@alielagrebi/regularization-lasso-ridge-regression-105f426b749c) - [Elastic Net](https://medium.com/@vijay.swamy1/lasso-versus-ridge-versus-elastic-net-1d57cfc64b58) - [Least-Angle Regression (LARS)](https://medium.com/acing-ai/what-is-least-angle-regression-lar-bb86756f01d0) - Association Rule Learning Algorithms - [Apriori algorithm](https://www.digitalvidya.com/blog/apriori-algorithms-in-data-mining/) - [Eclat algorithm](https://medium.com/machine-learning-researcher/association-rule-apriori-and-eclat-algorithm-4e963fa972a4) - Ensemble Algorithms - [Random Forest](https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76) - [Boosting](https://medium.com/greyatom/a-quick-guide-to-boosting-in-ml-acf7c1585cb5) - [Bootstrapped Aggregation (Bagging)](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) - [AdaBoost](https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfe) - [Weighted Average (Blending)](https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/) - [Stacked Generalization (Stacking)](https://medium.com/weightsandbiases/an-introduction-to-model-ensembling-63effc2ca4b3) - [Gradient Boosting Machines (GBM)](https://towardsdatascience.com/understanding-gradient-boosting-machines-9be756fe76ab) - [Gradient Boosted Regression Trees (GBRT)](https://www.youtube.com/watch?v=3CC4N4z3GJc) - Artificial Neural Network Algorithms - [Perceptron](https://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53) - [Multilayer Perceptrons (MLP)](https://medium.com/@AI_with_Kain/understanding-of-multilayer-perceptron-mlp-8f179c4a135f) - [Back-Propagation](https://towardsdatascience.com/understanding-backpropagation-algorithm-7bb3aa2f95fd) - [Stochastic Gradient Descent](https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31) - [Hopfield Network](https://medium.com/@serbanliviu/hopfield-nets-and-the-brain-e5880070cdba) - [Radial Basis Function Network (RBFN)](https://towardsdatascience.com/radial-basis-functions-neural-networks-all-we-need-to-know-9a88cc053448) - Deep Learning Neural Network Algorithms - [Convolutional Neural Network (CNN)](https://towardsdatascience.com/simple-introduction-to-convolutional-neural-networks-cdf8d3077bac) - [Recurrent Neural Networks (RNNs)](https://towardsdatascience.com/learn-how-recurrent-neural-networks-work-84e975feaaf7) - [Long Short-Term Memory Networks (LSTMs)](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21) - [Stacked Auto-Encoders](https://medium.com/@venkatakrishna.jonnalagadda/sparse-stacked-and-variational-autoencoder-efe5bfe73b64) - [Deep Boltzmann Machine (DBM)](https://towardsdatascience.com/restricted-boltzmann-machines-simplified-eab1e5878976) - [Deep Belief Networks (DBN)](https://medium.com/analytics-army/deep-belief-networks-an-introduction-1d52bb867a25) - [Generative Adversarial Networks (GANs)](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f) - Dimensionality Reduction Algorithms - [Principal Component Analysis (PCA)](https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c) - [Principal Component Regression (PCR)](https://en.wikipedia.org/wiki/Principal_component_regression) - [Partial Least Squares Regression (PLSR)](https://en.wikipedia.org/wiki/Partial_least_squares_regression) - [Linear Discriminant Analysis (LDA)](https://medium.com/@srishtisawla/linear-discriminant-analysis-d38decf48105) - [Sammon Mapping](https://iq.opengenus.org/principle-of-sammon-mapping/) - [Multidimensional Scaling (MDS)](https://medium.com/datadriveninvestor/the-multidimensional-scaling-mds-algorithm-for-dimensionality-reduction-9211f7fa5345) - [Projection Pursuit](https://towardsdatascience.com/interesting-projections-where-pca-fails-fe64ddca73e6) ## Credits - Articles from [machine learning Mastery](https://machinelearningmastery.com/). (Inspired from) - Articles from [towardsdatascience](https://towardsdatascience.com/). (Credit goes to the respective authors) - Articles from [medium](https://medium.com/). (Credit goes to the respective authors) - Articles from [wikipedia](https://en.wikipedia.org/). - Articles from [kdnuggets](https://www.kdnuggets.com/). - Articles from [probabilisticworld](https://www.probabilisticworld.com/). - Articles from [digitalvidya](https://www.digitalvidya.com/). - Articles from [analyticsvidhya](https://www.analyticsvidhya.com/). - Videos from [youtube](https://www.youtube.com/). - Articles from [opengenus](https://iq.opengenus.org/).