{"id":22832635,"url":"https://github.com/timkong21/machine-learning-specialization","last_synced_at":"2026-05-11T05:48:22.930Z","repository":{"id":190841038,"uuid":"521765470","full_name":"TimKong21/Machine-Learning-Specialization","owner":"TimKong21","description":"Contains solutions and notes for the Machine Learning Specialization by Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG","archived":false,"fork":false,"pushed_at":"2023-02-02T16:26:35.000Z","size":55434,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-06T07:21:27.993Z","etag":null,"topics":["artificial-neural-networks","decision-trees","deep-learning","gradient-descent","linear-regression","logistic-regression","machine-learning","neural-network","python","recommender-system","regularization","supervised-learning","tensorflow","tree-ensembles","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/TimKong21.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":null,"governance":null}},"created_at":"2022-08-05T20:15:59.000Z","updated_at":"2024-11-28T15:21:53.000Z","dependencies_parsed_at":"2023-08-26T17:50:30.353Z","dependency_job_id":null,"html_url":"https://github.com/TimKong21/Machine-Learning-Specialization","commit_stats":null,"previous_names":["timkong21/machine-learning-specialization"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimKong21%2FMachine-Learning-Specialization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimKong21%2FMachine-Learning-Specialization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimKong21%2FMachine-Learning-Specialization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TimKong21%2FMachine-Learning-Specialization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TimKong21","download_url":"https://codeload.github.com/TimKong21/Machine-Learning-Specialization/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246403902,"owners_count":20771530,"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":["artificial-neural-networks","decision-trees","deep-learning","gradient-descent","linear-regression","logistic-regression","machine-learning","neural-network","python","recommender-system","regularization","supervised-learning","tensorflow","tree-ensembles","xgboost"],"created_at":"2024-12-12T21:08:23.554Z","updated_at":"2026-05-11T05:48:22.887Z","avatar_url":"https://github.com/TimKong21.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Machine Learning Specialization\n\nA brief description of what this project does and who it's for\nThe Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. \n\nIt provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)\n\nFor more information, pleaese refer to [Coursera](https://www.coursera.org/specializations/machine-learning-introduction?).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimkong21%2Fmachine-learning-specialization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimkong21%2Fmachine-learning-specialization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimkong21%2Fmachine-learning-specialization/lists"}