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Title: GitHub - Sahith02/machine-learning-algorithms: A curated list of all machine learning algorithms and deep learning algorithms grouped by category. · GitHub

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Description: A curated list of all machine learning algorithms and deep learning algorithms grouped by category. - Sahith02/machine-learning-algorithms

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https://github.com/Sahith02/machine-learning-algorithms#list-of-algorithms
Linear Regressionhttps://towardsdatascience.com/linear-regression-using-least-squares-a4c3456e8570
Logistic Regressionhttps://medium.com/data-science-group-iitr/logistic-regression-simplified-9b4efe801389
Stepwise Regressionhttps://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
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
k-Meanshttps://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1
Expectation Maximisation (EM)https://medium.com/@chloebee/the-em-algorithm-explained-52182dbb19d9
Hierarchical Clusteringhttps://www.kdnuggets.com/2019/09/hierarchical-clustering.html
Naive Bayeshttps://towardsdatascience.com/naive-bayes-explained-9d2b96f4a9c0
Gaussian Naive Bayeshttps://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/
Conditional Decision Treeshttps://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 C5.0https://towardsdatascience.com/what-is-the-c4-5-algorithm-and-how-does-it-work-2b971a9e7db0
Ridge Regressionhttps://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 Nethttps://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
Apriori algorithmhttps://www.digitalvidya.com/blog/apriori-algorithms-in-data-mining/
Eclat algorithmhttps://medium.com/machine-learning-researcher/association-rule-apriori-and-eclat-algorithm-4e963fa972a4
Random Foresthttps://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76
Boostinghttps://medium.com/greyatom/a-quick-guide-to-boosting-in-ml-acf7c1585cb5
Bootstrapped Aggregation (Bagging)https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205
AdaBoosthttps://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
Perceptronhttps://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53
Multilayer Perceptrons (MLP)https://medium.com/@AI_with_Kain/understanding-of-multilayer-perceptron-mlp-8f179c4a135f
Back-Propagationhttps://towardsdatascience.com/understanding-backpropagation-algorithm-7bb3aa2f95fd
Stochastic Gradient Descenthttps://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31
Hopfield Networkhttps://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
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-Encodershttps://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
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 Mappinghttps://iq.opengenus.org/principle-of-sammon-mapping/
Multidimensional Scaling (MDS)https://medium.com/datadriveninvestor/the-multidimensional-scaling-mds-algorithm-for-dimensionality-reduction-9211f7fa5345
Projection Pursuithttps://towardsdatascience.com/interesting-projections-where-pca-fails-fe64ddca73e6
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