{"id":13440291,"url":"https://github.com/zotroneneis/machine_learning_basics","last_synced_at":"2025-04-09T06:01:44.505Z","repository":{"id":29972028,"uuid":"122043796","full_name":"zotroneneis/machine_learning_basics","owner":"zotroneneis","description":"Plain python implementations of basic machine learning algorithms","archived":false,"fork":false,"pushed_at":"2024-06-27T11:21:33.000Z","size":11349,"stargazers_count":4367,"open_issues_count":0,"forks_count":840,"subscribers_count":164,"default_branch":"master","last_synced_at":"2025-04-02T05:01:38.758Z","etag":null,"topics":["algorithm","ipynb","k-nearest-neighbor","k-nearest-neighbours","k-nn","kmeans","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","neural-network","neural-networks","perceptron","python","python-implementations","python3"],"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/zotroneneis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":"support_vector_machines.ipynb","governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":"zotroneneis"}},"created_at":"2018-02-19T09:55:58.000Z","updated_at":"2025-04-01T10:06:00.000Z","dependencies_parsed_at":"2024-09-11T12:32:40.421Z","dependency_job_id":"0d4059ac-4ad3-4507-a5a8-cc5cd9d9c291","html_url":"https://github.com/zotroneneis/machine_learning_basics","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/zotroneneis%2Fmachine_learning_basics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zotroneneis%2Fmachine_learning_basics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zotroneneis%2Fmachine_learning_basics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zotroneneis%2Fmachine_learning_basics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zotroneneis","download_url":"https://codeload.github.com/zotroneneis/machine_learning_basics/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247987181,"owners_count":21028891,"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":["algorithm","ipynb","k-nearest-neighbor","k-nearest-neighbours","k-nn","kmeans","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","neural-network","neural-networks","perceptron","python","python-implementations","python3"],"created_at":"2024-07-31T03:01:21.396Z","updated_at":"2025-04-09T06:01:44.449Z","avatar_url":"https://github.com/zotroneneis.png","language":"Jupyter Notebook","readme":"# Machine learning basics\n\nThis repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, *not* to provide the most efficient implementations. \n\n- [Bayesian Linear Regression](bayesian_linear_regression.ipynb)\n- [Decision tree for classification](decision_tree_classification.ipynb)\n- [Decision tree for regression](decision_tree_regression.ipynb)\n- [k-nearest-neighbor](k_nearest_neighbour.ipynb)\n- [k-Means clustering](kmeans.ipynb)\n- [Linear Regression](linear_regression.ipynb)\n- [Logistic Regression](logistic_regression.ipynb)\n- [Multinomial Logistic Regression](softmax_regression.ipynb)\n- [Perceptron](perceptron.ipynb)\n- [Principal Component Analysis]([principal_component_analysis.ipynb)\n- [Simple neural network with one hidden layer](simple_neural_net.ipynb)\n- [Softmax regression](softmax_regression.ipynb)\n- [Support vector machines](support_vector_machines.ipynb)\n  \n  \n![alt text](figures/decision_tree_predictions.png)\n\n\n## Data preprocessing\n\nAfter several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, *not* to provide the most efficient implementations. \n\n- [Image preprocessing](image_preprocessing.ipynb)\n- [Preprocessing a numerical/categorical dataset](data_preprocessing.ipynb)\n\n![alt text](figures/image_preprocessing.png)\n\n\n## Live demo\nRun the notebooks online without having to clone the repository or install jupyter: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zotroneneis/machine_learning_basics/HEAD).   \n   \nNote: this does not work for the `data_preprocessing.ipynb` and `image_preprocessing.ipynb` notebooks because they require downloading a dataset first.\n\n## Feedback\n\nIf you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue.\n\n## License\n\nSee the LICENSE file for license rights and limitations (MIT).\n","funding_links":["https://github.com/sponsors/zotroneneis"],"categories":["Jupyter Notebook","How To (Python)","Uncategorized","📈Machine Learning"],"sub_categories":["Uncategorized","📖 Blog Tutorials"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzotroneneis%2Fmachine_learning_basics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzotroneneis%2Fmachine_learning_basics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzotroneneis%2Fmachine_learning_basics/lists"}