{"id":24793488,"url":"https://github.com/phenomsg/ml-notebook","last_synced_at":"2026-04-13T23:31:28.623Z","repository":{"id":272497902,"uuid":"659733208","full_name":"PhenomSG/ml-notebook","owner":"PhenomSG","description":"This project is designed for personal learning and exploration of fundamental machine learning concepts.","archived":false,"fork":false,"pushed_at":"2025-04-30T08:33:00.000Z","size":41596,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-30T09:47:52.236Z","etag":null,"topics":["decision-trees","linear-regression","logistic-regression","machine-learning","model-evaluation-metrics","neural-network","opencv","pandas","python3","recommendation-system","sckit-learn","supervised-machine-learning","tensorflow2","unsupervised-machine-learning"],"latest_commit_sha":null,"homepage":"https://phenomsg.github.io/ml-notebook/","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/PhenomSG.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":"2023-06-28T12:50:45.000Z","updated_at":"2025-04-30T08:33:04.000Z","dependencies_parsed_at":"2025-01-14T20:53:58.525Z","dependency_job_id":null,"html_url":"https://github.com/PhenomSG/ml-notebook","commit_stats":null,"previous_names":["phenomsg/ml-notebook"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/PhenomSG/ml-notebook","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2Fml-notebook","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2Fml-notebook/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2Fml-notebook/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2Fml-notebook/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PhenomSG","download_url":"https://codeload.github.com/PhenomSG/ml-notebook/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2Fml-notebook/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31775707,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-13T20:17:16.280Z","status":"ssl_error","status_checked_at":"2026-04-13T20:17:08.216Z","response_time":93,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["decision-trees","linear-regression","logistic-regression","machine-learning","model-evaluation-metrics","neural-network","opencv","pandas","python3","recommendation-system","sckit-learn","supervised-machine-learning","tensorflow2","unsupervised-machine-learning"],"created_at":"2025-01-29T21:57:19.483Z","updated_at":"2026-04-13T23:31:28.615Z","avatar_url":"https://github.com/PhenomSG.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ML\nLink: https://www.tutorialspoint.com/machine_learning/index.htm\nWelcome to the ML-Basics repository! This project is designed for personal learning and exploration of fundamental machine learning concepts. It covers a variety of topics, from basic data preprocessing to implementing different machine learning algorithms using popular libraries like Scikit-learn, TensorFlow, and PyTorch.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Getting Started](#getting-started)\n  - [Prerequisites](#prerequisites)\n  - [Installation](#installation)\n- [Directory Structure](#directory-structure)\n- [Topics Covered](#topics-covered)\n  - [Data Preprocessing](#data-preprocessing)\n  - [Supervised Learning](#supervised-learning)\n  - [Unsupervised Learning](#unsupervised-learning)\n  - [Neural Networks](#neural-networks)\n  - [Model Evaluation](#model-evaluation)\n- [License](#license)\n\n## Introduction\n\nThis repository serves as a comprehensive guide for anyone starting out in machine learning. It includes step-by-step tutorials, code examples, and detailed explanations of various ML techniques and algorithms.\n\n## Getting Started\n\n### Prerequisites\n\nTo get the most out of this repository, you should have a basic understanding of Python programming and some familiarity with statistics and linear algebra. Additionally, you will need the following software installed:\n\n- Python 3.7 or higher\n- Jupyter Notebook\n- Git\n\n### Installation\n\n1. Clone the repository:\n    ```sh\n    git clone https://github.com/PhenomSG/ML-Basics.git\n    ```\n2. Navigate to the project directory:\n    ```sh\n    cd ML-Basics\n    ```\n3. Create a virtual environment:\n    ```sh\n    python -m venv env\n    ```\n4. Activate the virtual environment:\n    - On Windows:\n        ```sh\n        .\\env\\Scripts\\activate\n        ```\n    - On macOS and Linux:\n        ```sh\n        source env/bin/activate\n        ```\n5. Install the required packages:\n    ```sh\n    pip install -r requirements.txt\n    ```\n\n\n## Topics Covered\n\n### Data Preprocessing\n\n- Handling missing values\n- Feature scaling and normalization\n- Encoding categorical variables\n\n### Supervised Learning\n\n- Linear Regression\n- Logistic Regression\n- Decision Trees\n- Random Forests\n- Support Vector Machines\n\n### Unsupervised Learning\n\n- K-means Clustering\n- Hierarchical Clustering\n- Principal Component Analysis (PCA)\n\n### Neural Networks\n\n- Introduction to neural networks\n- Building neural networks with TensorFlow and Keras\n- Training and evaluating neural networks\n\n### Model Evaluation\n\n- Cross-validation\n- Confusion matrix\n- ROC curves and AUC\n- Precision, recall, and F1 score\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fml-notebook","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphenomsg%2Fml-notebook","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fml-notebook/lists"}