{"id":22733277,"url":"https://github.com/duongnmanh/ibm_ai_issue","last_synced_at":"2026-01-28T20:05:11.252Z","repository":{"id":266525305,"uuid":"898003101","full_name":"DuongNManh/IBM_AI_issue","owner":"DuongNManh","description":"My learning of AI","archived":false,"fork":false,"pushed_at":"2025-01-20T08:38:29.000Z","size":27,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-05T13:47:39.101Z","etag":null,"topics":["artificial-intelligence","learning-materials"],"latest_commit_sha":null,"homepage":"","language":null,"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/DuongNManh.png","metadata":{"files":{"readme":null,"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,"zenodo":null}},"created_at":"2024-12-03T16:05:45.000Z","updated_at":"2025-01-20T08:38:31.000Z","dependencies_parsed_at":"2024-12-04T18:32:22.386Z","dependency_job_id":"0f65ae16-ec04-4171-baaa-11deac5ddf7e","html_url":"https://github.com/DuongNManh/IBM_AI_issue","commit_stats":null,"previous_names":["duongnmanh/ibm_ai_issue"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/DuongNManh/IBM_AI_issue","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DuongNManh%2FIBM_AI_issue","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DuongNManh%2FIBM_AI_issue/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DuongNManh%2FIBM_AI_issue/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DuongNManh%2FIBM_AI_issue/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DuongNManh","download_url":"https://codeload.github.com/DuongNManh/IBM_AI_issue/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DuongNManh%2FIBM_AI_issue/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28850474,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-28T15:15:36.453Z","status":"ssl_error","status_checked_at":"2026-01-28T15:15:13.020Z","response_time":57,"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":["artificial-intelligence","learning-materials"],"created_at":"2024-12-10T20:13:30.880Z","updated_at":"2026-01-28T20:05:11.247Z","avatar_url":"https://github.com/DuongNManh.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🌟 **IBM AI Issue**\n\n## 🚀 **Python Library for Machine Learning**\nThe `scikit-learn` package allows you to complete machine learning tasks with just a few lines of code.\n\n![Scikit-learn ML](https://github.com/user-attachments/assets/dafc4799-d3b4-4e5c-9947-6d7acec8f3fa)\n\n---\n\n## 🤖 **Supervised and Unsupervised Algorithms**\n\n![Algorithm Types](https://github.com/user-attachments/assets/cfc9b6fc-978a-4d8f-b7c8-9a6ae2cdc581)\n\n---\n\n### 📘 **Supervised Learning**\nSupervised learning involves training a model using labeled data. For example, consider a **cancer dataset**:\n\n![Cancer Dataset](https://github.com/user-attachments/assets/708977db-09b3-4fbf-8d4e-66b84503ec84)\n\n#### 🧠 **Types of Supervised Learning**\n1. **Classification**  \n   Classification predicts discrete categories or classes for a given input.\n\n   ![Classification Example](https://github.com/user-attachments/assets/3d8142ec-f445-4906-b9a9-cab78c16e924)\n\n2. **Regression**  \n   Regression predicts continuous values based on input data.\n   Base on the **independent variable** to determite continuous value of **Dependent variable**\n   \n\n   ![Regression Example](https://github.com/user-attachments/assets/ab8407f7-af24-4cad-bdbc-44d5ce5c8608)\n   ![Regression Example](https://github.com/user-attachments/assets/ee05f076-bbd9-41b9-8812-1def7da438c2)\n   ![Regression Example](https://github.com/user-attachments/assets/f03bdfc0-d9aa-47d4-8202-7e1b9cc521d5)\n\n   - Types of regression models:\n     - Simple Regression: Simple Linear \u0026 Non-linear Regression\n       ---\n          \n     - Multiple Regression: Multiple Linear \u0026 Non-linear Regression\n       ---\n       \n      - Linear Regression: Simple Linear \u0026 Multiple Regression\n        ---\n         - Simple Linear\n           1.Simple Linear Regression representation\n           ![Simple Linear Regression representation](https://github.com/user-attachments/assets/455ead21-8ac5-4eae-b239-5f143ccbb932)\n           2.Find the best fit Linear\n           ![Find the best fit linear](https://github.com/user-attachments/assets/79da396b-42f4-4646-970a-26725300776c)\n           3.Estimating the parameters\n           ![Estimating the parameters](https://github.com/user-attachments/assets/a9c64f28-8edd-469c-bea0-ddbb6ec64722)\n           4.Predict with linear regression\n           ![image](https://github.com/user-attachments/assets/1ad70d32-5808-426d-9c99-1ab25c400ee8)\n\n         - Multiple Linear\n          1.Multiple Linear Regression representation\n          ![Multiple Linear Regression representation](https://github.com/user-attachments/assets/736b8906-409e-4a18-862f-57159b9b4d06)\n          2.Expose the errors in the model\n          ![Expose the errors in the model](https://github.com/user-attachments/assets/d7bde9d9-79a3-4680-9267-682287ebe688)\n          3.Estimating the parameters\n          ![Estimating the parameters](https://github.com/user-attachments/assets/59be527b-e05a-4b75-bc88-9d1ed4531506)\n          ![Estimating the parameters](https://github.com/user-attachments/assets/b81b36f8-be99-495e-a9e7-cb2977c8fa05)\n        ---\n           **Question:**\n           ![Question](https://github.com/user-attachments/assets/dfa85da0-65d0-4109-af8a-88a7560d77e9)\n\n   - Application:\n      ![Regression Application](https://github.com/user-attachments/assets/ae1a171c-0545-468c-8c2d-522fa834a3bd)\n---\n\n### 📙 **Unsupervised Learning**\nUnsupervised learning works with unlabeled data to find hidden patterns or structures in the dataset.\n\n![Unsupervised Learning](https://github.com/user-attachments/assets/a2867f55-062b-4176-927f-94964f877ffe)\n\n#### 🧩 **Dataset for Unsupervised Learning**\nUnsupervised learning uses **unlabeled data**:\n\n![Unlabeled Dataset Example](https://github.com/user-attachments/assets/4cf7b563-175c-46fd-bc55-21ef3e207bd8)\n\n---\n\n#### 🔍 **Types of Unsupervised Learning**\n1. **Clustering**  \n   Group similar data points into clusters.\n\n   ![Clustering Example](https://github.com/user-attachments/assets/7647fa00-5a23-48cf-b8c2-d09c36fed914)\n\n2. **Dimensionality Reduction**  \n   Reduce the number of variables while retaining essential information.\n\n---\n\n#### 🧠 **Model evaluation**\n**1. Caculate the accurency of the model (how can this model predict an unknown dataset)**\n   - **Using a portion of the dataset**: train the model by entire dataset (labeled) and check by part of unlabeled data in same dataset\n     ![accurency of the model](https://github.com/user-attachments/assets/f18d3d70-e0a6-4c8f-a358-13556cdf33bd)\n     ![accurency of the model](https://github.com/user-attachments/assets/aeaff0db-c0a7-4fc7-915a-893f04ad32b0)\n\n   - **Training \u0026 out-of-sample Accuracy**\n     - **Training Accuracy**: % of correct predictions that the model makes when using the test dataset.\n       - when we train and testing on the same dataset =\u003e produces a high training accuracy\n         ![Training accuracy](https://github.com/user-attachments/assets/66fed1b6-d9be-416f-b280-8a80eed9e8ff)\n\n     - **Out-of-Sample Accuracy**: % of correct predictions that the model makes when using the unknown data.\n       ![Out-of-Sample Accuracy](https://github.com/user-attachments/assets/0ea43539-9584-4448-9c81-62d121ed1132)\n\n     - **Split train/test evaluation approach**: reduce the overfit and can evaluate the Out-of-Sample Accuracy of the model\n       ![Split train/test evaluation approach](https://github.com/user-attachments/assets/5c5aad1f-1bc7-467e-873a-71b98ece44bd)\n       ![Split train/test evaluation approach](https://github.com/user-attachments/assets/65bbc8b2-6caa-4844-900a-e554ecc61d34)\n\n     - **K-fold cross-validation**: splitting the dataset into K equally sized subsets. The model is trained on K-1 folds and tested on the remaining fold.\n       Result = avg of all test accuracy\n       ![K-fold cross-validation](https://github.com/user-attachments/assets/197b0574-c176-4c36-a2c7-d24cd2c9bd73)\n\n\n\n\n\n\n     \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fduongnmanh%2Fibm_ai_issue","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fduongnmanh%2Fibm_ai_issue","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fduongnmanh%2Fibm_ai_issue/lists"}