{"id":21082820,"url":"https://github.com/jxareas/machine-learning-notebooks","last_synced_at":"2025-05-16T13:07:03.971Z","repository":{"id":129679756,"uuid":"523599292","full_name":"jxareas/Machine-Learning-Notebooks","owner":"jxareas","description":"The full collection of Jupyter Notebook labs from Andrew Ng's Machine Learning Specialization.","archived":false,"fork":false,"pushed_at":"2025-03-19T07:49:25.000Z","size":27692,"stargazers_count":283,"open_issues_count":0,"forks_count":100,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-02T05:09:05.816Z","etag":null,"topics":["clustering","deep-learning","jupyter-notebook","kmeans","learn","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","neural-network","numpy","python","regression","reinforcement-learning","reinforcement-learning-algorithms","supervised-learning","tensorflow","unsupervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"unlicense","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jxareas.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}},"created_at":"2022-08-11T05:40:39.000Z","updated_at":"2025-03-31T12:05:14.000Z","dependencies_parsed_at":"2023-04-03T11:17:52.339Z","dependency_job_id":null,"html_url":"https://github.com/jxareas/Machine-Learning-Notebooks","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/jxareas%2FMachine-Learning-Notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jxareas%2FMachine-Learning-Notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jxareas%2FMachine-Learning-Notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jxareas%2FMachine-Learning-Notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jxareas","download_url":"https://codeload.github.com/jxareas/Machine-Learning-Notebooks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247987285,"owners_count":21028895,"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":["clustering","deep-learning","jupyter-notebook","kmeans","learn","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","neural-network","numpy","python","regression","reinforcement-learning","reinforcement-learning-algorithms","supervised-learning","tensorflow","unsupervised-learning"],"created_at":"2024-11-19T20:15:23.585Z","updated_at":"2025-04-09T06:09:35.970Z","avatar_url":"https://github.com/jxareas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ca name=\"readme-top\"\u003e\u003c/a\u003e\n\u003cbr /\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"#\"\u003e\n   \u003c!-- Replace this logo for a custom official logo --\u003e\n    \u003cimg src=\"./assets/images/ml-specialization.png\" alt=\"Machine Learning Specialization\" width=\"550\" height=\"300\"\u003e\n  \u003c/a\u003e\n\n\u003ch1 align = \"center\"\u003e\n\u003cb\u003e\u003ci\u003eMachine Learning Notebooks\u003c/i\u003e\u003c/b\u003e\n\u003c/h1\u003e\n    \u003c!-- Add/Remove categories depending on your project --\u003e\n  \u003cp align=\"center\"\u003e\n    Notebooks from the Machine Learning Specialization\n    \u003cbr /\u003e\n    \u003c!-- IMPORTANT NOTE: If you want to append emojis you'll need to add the '-' sign before and after the header, as shown below:  --\u003e\n    \u003ca href=\"#-modules-\"\u003eModules\u003c/a\u003e\n    ·\n    \u003ca href=\"#-license-\"\u003eLicense\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\nBreak into AI with the free-to-audit [Machine Learning Specialization][MACHINE_LEARNING_SPECIALIZATION_URL].\nMaster fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program\nby AI visionary Andrew Ng.\n\n**What you'll learn:**\n- **Build ML models with NumPy \u0026 scikit-learn**  \n  - Build \u0026 train supervised models for prediction \u0026 binary classification tasks (linear, logistic regression)\n\n- **Build \u0026 train a neural network with TensorFlow**  \n  - Perform multi-class classification  \n  - Build \u0026 use decision trees \u0026 tree ensemble methods  \n\n- **Apply best practices for ML development**  \n  - Use unsupervised learning techniques, including clustering \u0026 anomaly detection  \n\n- **Build recommender systems**  \n  - Use a collaborative filtering approach  \n  - Implement a content-based deep learning method  \n  - Build a deep reinforcement learning model  \n\n\n## 🚀 Modules 🚀\n\n### Course 1 - [Supervised Machine Learning: Regression and Classification][SUPERVISED_LEARNING_COURSE_URL]\n\nIn the first course of the Machine Learning Specialization, you will:\n\n- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.\n- Build and train supervised machine learning models for prediction and binary classification tasks, including linear\n  regression and logistic regression\n\n### Course 2 - [Advanced Learning Algorithms][ADVANCED_LEARNING_COURSE_URL]\n\nIn the second course of the Machine Learning Specialization, you will:\n\n- Build and train a neural network with TensorFlow to perform multi-class classification\n- Apply best practices for machine learning development so that your models generalize to data and tasks in the real\n  world\n- Build and use decision trees and tree ensemble methods, including random forests and boosted trees\n\n### Course 3 - [Unsupervised Learning, Recommenders, Reinforcement Learning][UNSUPERVISED_LEARNING_COURSE_URL]\n\nIn the third course of the Machine Learning Specialization, you will:\n\n- Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.\n- Build recommender systems with a collaborative filtering approach and a content-based deep learning method.\n- Build a deep reinforcement learning model.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n## 📜 License 📜\n\n```\nThis is free and unencumbered software released into the public domain.\n\nAnyone is free to copy, modify, publish, use, compile, sell, or\ndistribute this software, either in source code form or as a compiled\nbinary, for any purpose, commercial or non-commercial, and by any\nmeans.\n\nIn jurisdictions that recognize copyright laws, the author or authors\nof this software dedicate any and all copyright interest in the\nsoftware to the public domain. We make this dedication for the benefit\nof the public at large and to the detriment of our heirs and\nsuccessors. We intend this dedication to be an overt act of\nrelinquishment in perpetuity of all present and future rights to this\nsoftware under copyright law.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR\nOTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\nARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\nOTHER DEALINGS IN THE SOFTWARE.\n\nFor more information, please refer to \u003chttp://unlicense.org/\u003e\n```\n\n[MACHINE_LEARNING_SPECIALIZATION_URL]: https://www.coursera.org/specializations/machine-learning-introduction#courses\n\n[SUPERVISED_LEARNING_COURSE_URL]: https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction\n\n[ADVANCED_LEARNING_COURSE_URL]: https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction\n\n[UNSUPERVISED_LEARNING_COURSE_URL]: https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjxareas%2Fmachine-learning-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjxareas%2Fmachine-learning-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjxareas%2Fmachine-learning-notebooks/lists"}