{"id":19159945,"url":"https://github.com/eneskemalergin/machinelearning_beyond","last_synced_at":"2025-05-07T09:15:53.329Z","repository":{"id":86851169,"uuid":"44113611","full_name":"eneskemalergin/MachineLearning_Beyond","owner":"eneskemalergin","description":"Repository to store machine learning, artificial intelligence, and deep learning implementations with explanations","archived":false,"fork":false,"pushed_at":"2018-04-17T09:19:13.000Z","size":8781,"stargazers_count":11,"open_issues_count":0,"forks_count":2,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-05-07T09:15:42.799Z","etag":null,"topics":["algorithms","machine-learning"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eneskemalergin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2015-10-12T14:45:30.000Z","updated_at":"2024-12-16T19:12:34.000Z","dependencies_parsed_at":"2023-03-13T19:50:02.170Z","dependency_job_id":null,"html_url":"https://github.com/eneskemalergin/MachineLearning_Beyond","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eneskemalergin%2FMachineLearning_Beyond","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eneskemalergin%2FMachineLearning_Beyond/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eneskemalergin%2FMachineLearning_Beyond/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eneskemalergin%2FMachineLearning_Beyond/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eneskemalergin","download_url":"https://codeload.github.com/eneskemalergin/MachineLearning_Beyond/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252847524,"owners_count":21813458,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithms","machine-learning"],"created_at":"2024-11-09T08:52:50.780Z","updated_at":"2025-05-07T09:15:53.304Z","avatar_url":"https://github.com/eneskemalergin.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning and Beyond\n\nThe Machine Learning algorithms are broken down in several categories. In the following mind map from [Data Science Central](http://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms-1) we can see the summary:\n\n![](http://api.ning.com/files/0qR8BrPZ-VZNxGOCc9HIXhtVdu4FIxXA3BKWwtrYkXQ0nvVO1yOJKU76E4LcCd3ln-bdReqYkhipURC00JjlWMTMOr340TB9/ML87i.png)\n\nIn this repository, I am going to follow the mind map and store the algorithms, class notes, real life applications, snippets/scripts, and more.\n\n### 0. Others\n  - Stanford Machine Learning Class\n\n### 1. Regression Algorithms\n  - Linear Regression\n  - Logistic Regression\n  - Stepwise Regression\n  - Multivariate Adaptive Regression Splines (MARS)\n  - Locally Estimated Scatterplot Smoothing (LOESS)\n\n### 2. Instance-based Algorithms\n  - k-Nearest Neighbour (kNN)\n  - Learning Vector Quantization (LVQ)\n  - Self-Organizing Map (SOM)\n  - Locally Weighted Learning (LWL)\n\n### 3. Regularization Algorithms\n  - Ridge Regression\n  - Least Absolute Shrinkage and Selection Operator (LASSO)\n  - Elastic Net\n  - Least-Angle Regression (LARS)\n\n### 4. Decision Tree Algorithms\n  - Classification and Regression Tree (CART)\n  - Iterative Dichotomiser 3 (ID3)\n  - C4.5 and C5.0 (different versions of a powerful approach)\n  - Chi-squared Automatic Interaction Detection (CHAID)\n  - Decision Stump\n  - M5\n  - Conditional Decision Trees\n\n### 5. Bayesian Algorithms\n  - Naive Bayes\n  - Gaussian Naive Bayes\n  - Multinomial Naive Bayes\n  - Averaged One-Dependence Estimators (AODE)\n  - Bayesian Belief Network (BBN)\n  - Bayesian Network (BN)\n\n### 6. Clustering Algorithms\n  - k-Means\n  - k-Medians\n  - Expectation Maximisation (EM)\n  - Hierarchical Clustering\n\n### 7. Association Rule Learning Algorithms\n  - Apriori algorithm\n  - Eclat algorithm\n\n### 8. Artificial Neural Network Algorithms\n  - Perceptron\n  - Back-Propagation\n  - Hopfield Network\n  - Radial Basis Function Network (RBFN)\n\n### 9. Deep Learning Algorithms\n  - Deep Boltzmann Machine (DBM)\n  - Deep Belief Networks (DBN)\n  - Convolutional Neural Network (CNN)\n  - Stacked Auto-Encoders\n\n### 10. Dimensionality Reduction Algorithms\n  - Principal Component Analysis (PCA)\n  - Principal Component Regression (PCR)\n  - Partial Least Squares Regression (PLSR)\n  - Sammon Mapping\n  - Multidimensional Scaling (MDS)\n  - Projection Pursuit\n  - Linear Discriminant Analysis (LDA)\n  - Mixture Discriminant Analysis (MDA)\n  - Quadratic Discriminant Analysis (QDA)\n  - Flexible Discriminant Analysis (FDA)\n\n### 11. Ensemble Algorithms\n  - Boosting\n  - Bootstrapped Aggregation (Bagging)\n  - AdaBoost\n  - Stacked Generalization (blending)\n  - Gradient Boosting Machines (GBM)\n  - Gradient Boosted Regression Trees (GBRT)\n  - Random Forest\n\n### 12. Other Algorithms\n  - Computational intelligence (evolutionary algorithms, etc.)\n  - Computer Vision (CV)\n  - Natural Language Processing (NLP)\n  - Recommender Systems\n  - Reinforcement Learning\n  - Graphical Models\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feneskemalergin%2Fmachinelearning_beyond","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feneskemalergin%2Fmachinelearning_beyond","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feneskemalergin%2Fmachinelearning_beyond/lists"}