{"id":22381019,"url":"https://github.com/alejandrolara11/machinelearningcourse","last_synced_at":"2026-04-11T13:04:32.418Z","repository":{"id":265560675,"uuid":"896261660","full_name":"AlejandroLara11/MachineLearningCourse","owner":"AlejandroLara11","description":"Machine Learning Basics: From Setup to Clustering","archived":false,"fork":false,"pushed_at":"2024-12-09T22:41:05.000Z","size":1151,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-01T01:30:49.905Z","etag":null,"topics":["data-analysis","data-science","machine-learning","numpy","pandas","plotly","preprocessing-data","python","scikit-learn","seaborn","streamlit"],"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/AlejandroLara11.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-29T22:48:01.000Z","updated_at":"2024-12-09T22:41:09.000Z","dependencies_parsed_at":"2025-02-01T01:29:32.776Z","dependency_job_id":"1c7bb736-52fe-44fd-9583-f27206320718","html_url":"https://github.com/AlejandroLara11/MachineLearningCourse","commit_stats":null,"previous_names":["alejandrolara11/machinelearningcourse"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FMachineLearningCourse","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FMachineLearningCourse/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FMachineLearningCourse/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlejandroLara11%2FMachineLearningCourse/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AlejandroLara11","download_url":"https://codeload.github.com/AlejandroLara11/MachineLearningCourse/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245720158,"owners_count":20661370,"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":["data-analysis","data-science","machine-learning","numpy","pandas","plotly","preprocessing-data","python","scikit-learn","seaborn","streamlit"],"created_at":"2024-12-05T00:07:00.910Z","updated_at":"2025-12-30T23:24:01.184Z","avatar_url":"https://github.com/AlejandroLara11.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MachineLearningCourse\nMachine Learning Basics: From Setup to Clustering\nWelcome to my repository for the \"Machine Learning Basics\" course! 🚀 This repository documents my journey into the world of Machine Learning, covering foundational concepts, practical data handling, and essential algorithms.\n\n📚 Course Content\nIntroduction to Machine Learning:\nWhat is Machine Learning?\nUnderstanding its applications and types (supervised, unsupervised).\nEnvironment Setup:\nSetting up a virtual environment for ML projects.\nData Preprocessing:\nBeginner-friendly techniques for data treatment.\nImproving workflows with pipelines.\nData Collection and Exploration:\nTechniques to gather and explore datasets.\nHandling missing values and outliers.\nNormalization and Encoding:\nScaling data for better model performance.\nEncoding categorical variables for ML algorithms.\nTraining and Testing:\nSplitting datasets into training and testing sets.\nUnderstanding the importance of cross-validation.\nModel Evaluation:\nMetrics for classification (e.g., precision, recall, F1-score).\nMetrics for regression (e.g., RMSE, R-squared).\nClustering Algorithms:\nK-Means clustering.\nHierarchical clustering and dendrograms.\n\n💻 What’s in this Repository?\nCode Implementations: Step-by-step Python notebooks for each topic.\nHands-On Practice: Exercises and challenges to reinforce learning.\nMini-Projects: Practical examples to apply concepts to real-world scenarios.\nNotes: Concise summaries of theoretical and practical lessons.\n🛠️ Technologies and Libraries\nPython 🐍\nLibraries: numpy, pandas, matplotlib, scikit-learn, seaborn, streamlit, plotly.\n🌟 Objectives\nDevelop a solid understanding of ML concepts and workflows.\nLearn essential data preprocessing techniques.\nGain hands-on experience with clustering and evaluation metrics.\nBuild confidence to explore more advanced Machine Learning topics.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falejandrolara11%2Fmachinelearningcourse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falejandrolara11%2Fmachinelearningcourse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falejandrolara11%2Fmachinelearningcourse/lists"}