{"id":25066067,"url":"https://github.com/benjaminpla/machine_learning","last_synced_at":"2026-05-02T03:32:56.191Z","repository":{"id":246973713,"uuid":"824295798","full_name":"benjaminPla/machine_learning","owner":"benjaminPla","description":"Machine Learning course by Digital House academy","archived":false,"fork":false,"pushed_at":"2024-07-07T13:51:37.000Z","size":4404,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-31T14:04:39.034Z","etag":null,"topics":["git","machine-learning","ml","python"],"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/benjaminPla.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":"2024-07-04T19:48:16.000Z","updated_at":"2024-07-08T21:06:39.000Z","dependencies_parsed_at":"2025-02-06T20:00:33.456Z","dependency_job_id":"4a06506e-8389-4511-888c-7544369ea542","html_url":"https://github.com/benjaminPla/machine_learning","commit_stats":null,"previous_names":["benjaminpla/machine_learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/benjaminPla/machine_learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benjaminPla%2Fmachine_learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benjaminPla%2Fmachine_learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benjaminPla%2Fmachine_learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benjaminPla%2Fmachine_learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/benjaminPla","download_url":"https://codeload.github.com/benjaminPla/machine_learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benjaminPla%2Fmachine_learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32522247,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-02T01:12:54.858Z","status":"online","status_checked_at":"2026-05-02T02:00:05.923Z","response_time":132,"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":["git","machine-learning","ml","python"],"created_at":"2025-02-06T20:00:06.230Z","updated_at":"2026-05-02T03:32:56.175Z","avatar_url":"https://github.com/benjaminPla.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning\n\n## Supervised Learning\n\nYou have the output column and you train the model with it.\n\n- **Classification**: Predict a class label or category (e.g., true/false, spam/ham, cat/dog).\n\n  - Logistic Regression (binary or multi-class)\n  - Decision Tree\n  - Random Forest\n  - Support Vector Machine (SVM)\n  - K-Nearest Neighbors (KNN)\n  - Naive Bayes\n  - Gradient Boosting Machines (GBM)\n  - Neural Networks\n\n- **Regression**: Predict a continuous value (e.g., price, temperature, age).\n\n  - Linear Regression\n  - Decision Tree Regression\n  - Random Forest Regression\n  - Support Vector Regression (SVR)\n  - K-Nearest Neighbors Regression\n  - Ridge Regression\n  - Lasso Regression\n  - Polynomial Regression\n  - Neural Networks (e.g., Multilayer Perceptron)\n\n## Unsupervised Learning\n\nYou don't have the output column and you train the model without it.\n\n- **Dimensionality Reduction**: Reduce the number of features while preserving important information.\n\n  - Principal Component Analysis (PCA)\n  - t-Distributed Stochastic Neighbor Embedding (t-SNE)\n  - Singular Value Decomposition (SVD)\n  - Linear Discriminant Analysis (LDA)\n  - Independent Component Analysis (ICA)\n\n- **Density Estimation**: Estimate the probability distribution of data.\n\n  - Gaussian Mixture Models (GMM)\n  - Kernel Density Estimation (KDE)\n\n- **Market Basket Analysis**: Identify associations between items.\n\n  - Apriori Algorithm\n  - Eclat Algorithm\n\n- **Clustering**: Group similar data points together.\n\n  - K-Means\n  - Hierarchical Clustering\n  - DBSCAN (Density-Based Spatial Clustering of Applications with Noise)\n  - Mean Shift\n\n# Metrics\n\n## Summary:\n\n- **Accuracy** gives an overall measure of correct predictions.\n- **Confusion Matrix** breaks down the types of correct and incorrect predictions.\n- **Recall** (or sensitivity) focuses on correctly identifying positive cases.\n- **Specificity** (or true negative rate) focuses on correctly identifying negative cases.\n- **F1 Score** balances precision and recall into a single metric, useful when there's an uneven class distribution.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenjaminpla%2Fmachine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenjaminpla%2Fmachine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenjaminpla%2Fmachine_learning/lists"}