{"id":19517806,"url":"https://github.com/m0hc3n/machine-learning-algorithms-from-scratch","last_synced_at":"2025-04-26T06:31:29.193Z","repository":{"id":183256516,"uuid":"662704338","full_name":"M0hc3n/Machine-Learning-Algorithms-From-Scratch","owner":"M0hc3n","description":"This repository gathers the essential Machine Learning algorithms coded from scratch using only numpy and sklearn ","archived":false,"fork":false,"pushed_at":"2023-10-10T08:58:31.000Z","size":36,"stargazers_count":12,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-04T09:11:23.322Z","etag":null,"topics":["machine-learning","ml","ml-algorithms","numpy","supervised-learning","unsupervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/M0hc3n.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":"support_vector_machine/main.py","governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-07-05T17:49:59.000Z","updated_at":"2025-02-12T18:13:41.000Z","dependencies_parsed_at":null,"dependency_job_id":"815e4d09-f92b-4b27-87ed-505a68dfea2a","html_url":"https://github.com/M0hc3n/Machine-Learning-Algorithms-From-Scratch","commit_stats":null,"previous_names":["mohcen2311/machine-learning-algorithms-from-scratch","m0hc3n/machine-learning-algorithms-from-scratch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/M0hc3n%2FMachine-Learning-Algorithms-From-Scratch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/M0hc3n%2FMachine-Learning-Algorithms-From-Scratch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/M0hc3n%2FMachine-Learning-Algorithms-From-Scratch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/M0hc3n%2FMachine-Learning-Algorithms-From-Scratch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/M0hc3n","download_url":"https://codeload.github.com/M0hc3n/Machine-Learning-Algorithms-From-Scratch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250944148,"owners_count":21511704,"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":["machine-learning","ml","ml-algorithms","numpy","supervised-learning","unsupervised-learning"],"created_at":"2024-11-11T00:06:33.707Z","updated_at":"2025-04-26T06:31:25.862Z","avatar_url":"https://github.com/M0hc3n.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine-Learning-Algorithms-From-Scratch\n\nThis repository gathers the essential Machine Learning algorithms coded from scratch using only: \n  - **Numpy**: for algebraic, and statistical operations\n  - **Sklearn**: for generating testing data\n\n## Getting Started:\n  - Start by setting up a python **virtual environment** by running: \n```bash\n   python -m virtual_env_name /path/to/new/virtual/environment\n```\n  - Activate the virtual environment:\n```bash\n   .\\virtual_env_name\\Scripts\\activate\n```\n  - Install the required libraries:\n```bash\n   pip install -r requirements.txt\n```\n  - All the folders contain at least two files:\n      - **model_name.py**: contains the class that implements a specific ML model or technique.\n      - **main.py**: contains the testing script, it usually has an accuracy check or a plotting of the result.\n    To test the implementation, you can drag and drop the main file to the main directory \\\n    \u003cbr /\u003e\n![Recording_2023-07-23_154017_AdobeExpress](https://github.com/Mohcen2311/Machine-Learning-Algorithms-From-Scratch/assets/101293365/c67261cd-eec6-46e6-ac34-bdfe09e1d5c5)\n    \u003cbr /\u003e\n    then, you can run:\n```bash\n   python main.py\n```\n\n### Resources:\n  - The tutorial that engaged me in creating this repository is [this one](https://www.youtube.com/watch?v=rLOyrWV8gmA), it helps to understand the coding phase of the algorithms, and it contains pretty usefull testing scripts that I have used.\n  - Although the previous tutorial was mostly enriching, in the theoretical part, I have taken advantage of insightful blogs written in [Towards DataScience](https://towardsdatascience.com/), [Ask Python](https://www.askpython.com/), and [Wikipedia](https://www.wikipedia.org/).\n    I have included all the blogs that I have read to write the code implementation in its corresponding file.\n  - For people who like to visualize things, I recommend the following youtube channels: [StatQuest](https://www.youtube.com/@statquest), [Visually Explained](https://www.youtube.com/@VisuallyExplained), and [Intuitive Machine Learning](https://www.youtube.com/@IntuitiveMachineLearning).\n\n***Happy Learning!*** \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm0hc3n%2Fmachine-learning-algorithms-from-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm0hc3n%2Fmachine-learning-algorithms-from-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm0hc3n%2Fmachine-learning-algorithms-from-scratch/lists"}