{"id":19845758,"url":"https://github.com/jesussantana/ibm-machine-learning-with-python","last_synced_at":"2026-05-14T06:31:57.221Z","repository":{"id":113635546,"uuid":"371924940","full_name":"jesussantana/IBM-Machine-Learning-with-Python","owner":"jesussantana","description":"This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language","archived":false,"fork":false,"pushed_at":"2021-05-30T11:06:50.000Z","size":1432,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-28T22:51:37.144Z","etag":null,"topics":["clustering","data-science","decision-trees","dimensionality-reduction","machine-learning","python","random-forests","regression-models","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jesussantana.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":"2021-05-29T08:51:44.000Z","updated_at":"2021-06-07T13:35:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"3aeaeeff-906e-4c02-a3ee-37da93b19105","html_url":"https://github.com/jesussantana/IBM-Machine-Learning-with-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jesussantana/IBM-Machine-Learning-with-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FIBM-Machine-Learning-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FIBM-Machine-Learning-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FIBM-Machine-Learning-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FIBM-Machine-Learning-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jesussantana","download_url":"https://codeload.github.com/jesussantana/IBM-Machine-Learning-with-Python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FIBM-Machine-Learning-with-Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33013230,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-13T13:14:54.681Z","status":"online","status_checked_at":"2026-05-14T02:00:06.663Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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","data-science","decision-trees","dimensionality-reduction","machine-learning","python","random-forests","regression-models","unsupervised-learning"],"created_at":"2024-11-12T13:09:14.471Z","updated_at":"2026-05-14T06:31:57.207Z","avatar_url":"https://github.com/jesussantana.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IBM Pyhton for Data Science\n\n[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  \n[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge\u0026logo=Jupyter)](https://jupyter.org/try)  \n\n\n## This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.\n\n## Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!\n\n## Explore many algorithms and models:\n- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.\n- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.\n## ReferencesGet ready to do more learning than your machine! \n  \n  \n\n## COURSE SYLLABUS: \n\n### Module 1 - Supervised vs Unsupervised Learning\n\n- Machine Learning vs Statistical Modelling\n- Supervised vs Unsupervised Learning \n- Supervised Learning Classification \n- Unsupervised Learning \n\n### Module 2 - Supervised Learning I  \n\n- Regression Algorithms \n- Model Evaluation \n- Model Evaluation: Overfitting \u0026 Underfitting\n- Understanding Different Evaluation Models \n\n### Module 3 - Supervised Learning II\n\n- K-Nearest Neighbors \n- Decision Trees \n- Random Forests\n- Reliability of Random Forests \n- Advantages \u0026 Disadvantages of Decision Trees \n\n### Module 4 - Unsupervised Learning\n\n- K-Means Clustering plus Advantages \u0026 Disadvantages \n- Hierarchical Clustering plus Advantages \u0026 Disadvantages \n- Measuring the Distances Between Clusters - Single Linkage Clustering \n- Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering\n- Density-Based Clustering  \n\n### Module 5 - Dimensionality Reduction \u0026 Collaborative Filtering\n\n- Dimensionality Reduction: Feature Extraction \u0026 Selection \n- Collaborative Filtering \u0026 Its Challenges  \n\n### PREREQUISITES  \n\n- Python for data science\n\n## RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE  \n\n### You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.\n \n### This hands-on lab requires that you have working knowledge of Python programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with Python, I recommend you take Data Analysis with Python courses.\n\nhttps://cognitiveclass.ai/courses/machine-learning-with-python","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesussantana%2Fibm-machine-learning-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjesussantana%2Fibm-machine-learning-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesussantana%2Fibm-machine-learning-with-python/lists"}