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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of all machine learning algorithms and deep learning algorithms grouped by category.
| Date | Stars |
|---|---|
| 2026-07-24 | 383 |
| 2026-07-25 | 383 |
| 2026-07-28 | 383 |
| 2026-07-30 | 383 |
| 2026-07-31 | 385 |
| 2026-08-06 | 385 |
Today
— stars today
This week
+2 stars this week
This month
— stars this month
Momentum
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growth rate 0.52%/day
# 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-s
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:91a2479c58f2ed58, topic:deep-learning, topic:neural-network
matched fp:91a2479c58f2ed58, topic:tutorial, desc:curated list, readme:curated list