{"id":24087812,"url":"https://github.com/sanikamal/machine-learning-atoz","last_synced_at":"2026-06-08T22:31:11.210Z","repository":{"id":271814752,"uuid":"864424002","full_name":"sanikamal/machine-learning-atoz","owner":"sanikamal","description":"Beginner-friendly machine learning tutorials and mini-projects.","archived":false,"fork":false,"pushed_at":"2024-09-28T08:09:12.000Z","size":2083,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-21T22:02:53.286Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/sanikamal.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":"2024-09-28T07:17:39.000Z","updated_at":"2024-09-28T08:09:15.000Z","dependencies_parsed_at":"2025-01-10T03:54:56.677Z","dependency_job_id":"f41551ab-d70b-488d-9157-4d269bc110c1","html_url":"https://github.com/sanikamal/machine-learning-atoz","commit_stats":null,"previous_names":["sanikamal/machine-learning-atoz"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sanikamal/machine-learning-atoz","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanikamal%2Fmachine-learning-atoz","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanikamal%2Fmachine-learning-atoz/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanikamal%2Fmachine-learning-atoz/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanikamal%2Fmachine-learning-atoz/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sanikamal","download_url":"https://codeload.github.com/sanikamal/machine-learning-atoz/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanikamal%2Fmachine-learning-atoz/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34083848,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"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":["collaborative-filtering","data-analysis","data-visualization","decision-trees","kmeans-clustering","knn","machine-learning","machine-learning-algorithms","recommender-system","regression","svm"],"created_at":"2025-01-10T03:54:53.402Z","updated_at":"2026-06-08T22:31:11.186Z","avatar_url":"https://github.com/sanikamal.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning A to Z 🌟🚀\n\nA collection of awesome, beginner-friendly machine learning tutorials and projects. This repository provides hands-on examples, comprehensive explanations, and essential techniques for machine learning practitioners. Ideal for those starting in the field and looking to expand their knowledge through practical implementation.\n\n## Contents 📚\n\n| Title       | Description    | Technology/Library  | Link  | Article |\n|-------------|----------------|---------------------|-------|---------|\n| 5 Machine Learning Libraries        | Overview of the 5 machine learning libraries in Python for fast and efficient development. | `NumPy`, `Pandas`, `Matplotlib`,`NLTK`, `Scikit-learn`       |  [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/notebook/top_five_machine_learning_libraries_in_python.ipynb)     | Python Libraries for ML |\n| Simple Linear Regression | Implement simple linear regression to predict engine size on fuel consumption dataset.  | `Pandas`, `Matplotlib`, `Scikit-learn`    | [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/Simple-Linear-Regression.ipynb) | Basics of Linear Regression |\n| Spam Detection Using Naive-Bayes| Detect spam emails using Naive-Bayes classification techniques.  | `Scikit-learn`,`Pandas`| [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/spam-detection/spam-detection.ipynb)   | Spam Detection with Naive-Bayes |\n\u003c!--\n\n| Multiple Linear Regression         | Extend your knowledge with multiple linear regression, handling multiple input variables.     | Scikit-learn                |                                                          | Multiple Linear Regression Explained|\n| Model Evaluation in Regression     | Learn how to evaluate regression models using R², MAE, and other key metrics.                | Scikit-learn                |                                                          | Regression Model Evaluation         |\n| Non-Linear Regression              | Explore non-linear regression models, used when data relationships are not linear.           | Scikit-learn                | [View Notebook](https://github.com/sanikamal/awesome-ml-examples/blob/master/NoneLinearRegression.ipynb) | Understanding Non-Linear Regression |\n| K-Nearest Neighbors                | Classify data using the K-Nearest Neighbors (KNN) algorithm based on proximity.              | Scikit-learn                | [View Notebook](https://github.com/sanikamal/awesome-ml-examples/blob/master/K-Nearest-neighbors.ipynb) | KNN: A Simple Classifier            |\n| Decision Trees                     | Implement decision trees, an intuitive way to classify data based on feature splits.         | Scikit-learn                |                                                          | Decision Trees Explained            |\n| Evaluation Metrics in Classification| Gain insights into key classification metrics, such as accuracy, precision, recall, and F1-score. | Scikit-learn              |                                                          | Classification Evaluation           |\n| Logistic vs Linear Regression      | Compare and contrast logistic regression with linear regression to understand appropriate use cases. | Scikit-learn           |                                                          | Logistic vs Linear Regression       |\n| Support Vector Machines (SVM)      | Implement support vector machines, used to find the optimal boundary for separating classes. | Scikit-learn                |                                                          | Introduction to SVM                 |\n| K-Means Clustering                 | Partition your data into clusters using K-Means and explore the underlying clustering mechanisms. | Scikit-learn             |                                                          | Clustering with K-Means             |\n| Hierarchical Clustering            | Discover hierarchical clustering and how it creates a hierarchy of clusters with dendrograms. | Scikit-learn              |                                                          | Hierarchical Clustering Overview    |\n| DBSCAN                             | Learn about DBSCAN, a clustering algorithm that groups data points based on density, ideal for non-spherical data. | Scikit-learn             |                                                          | DBSCAN Algorithm Explained          |\n| Content-Based Recommender Systems  | Build content-based recommendation systems that suggest items based on user preferences.      | Scikit-learn, Pandas        |                                                          | Creating a Content-Based Recommender|\n| Collaborative Filtering            | Implement collaborative filtering for personalized recommendations based on user-item interactions. | Scikit-learn             |                                                          | Collaborative Filtering Techniques  | --\u003e\n\n## Contribution Guidelines\n\nWe welcome contributions to this project! Please follow the standard [contribution guidelines](CONTRIBUTING.md) to submit pull requests or raise issues.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanikamal%2Fmachine-learning-atoz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsanikamal%2Fmachine-learning-atoz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanikamal%2Fmachine-learning-atoz/lists"}