{"id":30344492,"url":"https://github.com/flexycode/ccmaclrl","last_synced_at":"2026-05-07T00:37:08.165Z","repository":{"id":309194965,"uuid":"1031646058","full_name":"flexycode/CCMACLRL","owner":"flexycode","description":"🤖 This repository is intended for our Machine Learning CCMACLRL COM231ML by Professor Elizer Ponio Jr","archived":false,"fork":false,"pushed_at":"2025-08-10T12:06:30.000Z","size":4,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-10T14:18:30.157Z","etag":null,"topics":["artificial-intelligence","linnear-regression","machine-learning","machine-learning-algorithms","python","random-forest","scikit-learn","supervised-learning","tensorflow"],"latest_commit_sha":null,"homepage":"","language":null,"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/flexycode.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,"zenodo":null}},"created_at":"2025-08-04T05:57:55.000Z","updated_at":"2025-08-10T12:14:41.000Z","dependencies_parsed_at":"2025-08-10T14:18:46.898Z","dependency_job_id":"0d176e41-a825-4e21-927c-09549591f20b","html_url":"https://github.com/flexycode/CCMACLRL","commit_stats":null,"previous_names":["flexycode/ccmaclrl"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/flexycode/CCMACLRL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/flexycode%2FCCMACLRL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/flexycode%2FCCMACLRL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/flexycode%2FCCMACLRL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/flexycode%2FCCMACLRL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/flexycode","download_url":"https://codeload.github.com/flexycode/CCMACLRL/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/flexycode%2FCCMACLRL/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270996247,"owners_count":24681933,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-18T02:00:08.743Z","response_time":89,"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":["artificial-intelligence","linnear-regression","machine-learning","machine-learning-algorithms","python","random-forest","scikit-learn","supervised-learning","tensorflow"],"created_at":"2025-08-18T12:42:33.865Z","updated_at":"2026-05-07T00:37:08.159Z","avatar_url":"https://github.com/flexycode.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# 💫 Introduction to Machine Learning \n\n\u003c!-- Background github cover with short introduction down below \n\u003cimg src=\"https://github.com/flexycode/CTINFMGL/blob/main/asset/Information-Management.png\" /\u003e\n--\u003e\n\n### Name: [Jay Arre Talosig](https://www.youtube.com/watch?v=-er2ruCgzjg\u0026list=RDfFqxDrmQLnQ\u0026index=4)  \n### Subject \u0026 Section: [CCMACLRL - COM231ML](https://www.youtube.com/watch?v=fFqxDrmQLnQ\u0026list=RDfFqxDrmQLnQ\u0026start_radio=1)\n### Schedule: [TUE 11:00AM - 01:40 PM VR09CCIT - FRI 11:00AM - 03:00 PM 408 MB](https://www.youtube.com/watch?v=dL7Vn7hJDAk\u0026list=RDdL7Vn7hJDAk\u0026start_radio=1)\n### Professor: [Elizer Ponio Jr](https://github.com/robitussin/)     \n### No. of Units: [3 Units](https://www.youtube.com/watch?v=UVJSA2N39NU\u0026list=RDUVJSA2N39NU\u0026start_radio=1)\n### Prerequisite: [Python \u0026 Common Sense](https://www.pornhub.com/)\n### Subject Repo Link: [Professor Elizer Machine Learning Repo](https://github.com/robitussin/CCMACLRL)\n### Project Link: [CCMACLRL_COM231_PROJECT](https://github.com/flexycode/CCMACLRL_COM231_PROJECT)\n### Lab Activity Link: [CCMACLRL_EXERCISES_COM231ML](https://github.com/flexycode/CCMACLRL_EXERCISES_COM231ML)\n\n\u003c!-- 🤖 Machine Learning 🤖 --\u003e\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPWVjZjA1ZTQ3N3lpMjVqNnE3dWh4Mzk0cnF4N2RhcWJudmxvc3RqMW0waHFiN3R5MCZlcD12MV9zdGlja2Vyc19zZWFyY2gmY3Q9cw/jY1r8EHyk4Ye9KUOUb/giphy.gif\" width=\"250\"\u003e\n\u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPWVjZjA1ZTQ3b3pjaDIydDdpZXBnZWRxMWVuOWMyeDV1dHU0c3N5N243eDcyaWVkZCZlcD12MV9zdGlja2Vyc19zZWFyY2gmY3Q9cw/rYchHXYdIDp3Qpt3IK/giphy.gif\" width=\"300\"\u003e\n\u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPWVjZjA1ZTQ3N3lpMjVqNnE3dWh4Mzk0cnF4N2RhcWJudmxvc3RqMW0waHFiN3R5MCZlcD12MV9zdGlja2Vyc19zZWFyY2gmY3Q9cw/jY1r8EHyk4Ye9KUOUb/giphy.gif\" width=\"250\"\u003e\n\u003c/div\u003e\n\n# 📜 Course Description\n\nThis course introduces students with a broad variety of fundamental statistical-based algorithms used to train models for basic predictive tasks. This course also covers the theoretical and mathematical concepts of each method complemented by hands-on activities.\n\n\n# Pre-requisites for this class\n\n- **Proficiency in one programming language**. All class assignments will be in Python. If you have a lot of programming experience but in a different language (e.g. Javascript/Java) you will probably be fine.\n- **College Calculus, Linear Algebra**. You should be comfortable taking derivatives and understanding matrix vector operations and notation.\n- **Basic Probability and Statistics.** You should know basics of probabilities, Gaussian distributions, mean, standard deviation, etc.\n\n# **🚀** Learning Outcomes\n\nBy the end of the class students should be able to:\n\n- Demonstrate sufficient knowledge in using various machine learning libraries and tools.\n- Understand foundational algorithms used for model building for machine learning.\n- Apply algorithms to a real-world problem using various machine learning libraries and tools, optimize the models learned, and report on the expected accuracy that can be achieved by applying the models.\n\n\n# 📅 Course Outline and Timeframe\n\n| Week | Topic | Mode of Delivery | Readings/Videos | Events |\n|------|-------|------------------|-----------------|--------|\n| **Week 1** | **Class Orientation**\u003cbr\u003e- Course Syllabus\u003cbr\u003e- Expectations for Online Classes / Class Setup\u003cbr\u003e- Grading and Deadlines\u003cbr\u003e- CAM\u003cbr\u003e\u003cbr\u003e**Class Setup for Machine Learning**\u003cbr\u003e- Introduction to Google Colab and Jupyter Notebook\u003cbr\u003e- Basic Python Tutorial | Online Lecture / Tutorial | **Google Colaboratory:** https://colab.research.google.com/\u003cbr\u003e**Get Started with Google Colab:**\u003cbr\u003ehttps://www.youtube.com/watch?v=inN8seMm7UI | |\n| **Week 2** | **Introduction to the Course**\u003cbr\u003e- History of AI and Machine Learning\u003cbr\u003e- Artificial Intelligence vs. Machine Learning\u003cbr\u003e- Taxonomy of ML\u003cbr\u003e- Goals and Limitations of ML\u003cbr\u003e- Real world applications | Lecture / Discussion | **What is Artificial Intelligence?**\u003cbr\u003ehttps://youtu.be/mJeNghZXtMo | **Exercise #1** |\n| **Week 3** | **K Nearest Neighbors**\u003cbr\u003e- Definition and Intuition\u003cbr\u003e- Hyperparameter (k)\u003cbr\u003e- Classification using k-NN\u003cbr\u003e- Applications | Lecture / Demo | **K-Nearest Neighbors Demo:**\u003cbr\u003ehttp://vision.stanford.edu/teaching/cs231n-demos/knn/ | **Exercise #2** |\n| **Week 4** | **Simple Linear Regression**\u003cbr\u003e- Equation of a line\u003cbr\u003e- Cost function intuition\u003cbr\u003e- Parameters\u003cbr\u003e- Implementation | Lecture / Hands-on | **An Introduction to Linear Regression Analysis:**\u003cbr\u003ehttps://www.youtube.com/watch?v=NUXdtN1W1FE | **Exercise #3** |\n| **Week 5** | **Multiple Linear Regression**\u003cbr\u003e- Model representation\u003cbr\u003e- Gradient descent for multiple variables\u003cbr\u003e- Feature scaling\u003cbr\u003e- Normal equation\u003cbr\u003e- Application | Lecture / Coding | **Linear Regression with Multiple Variables:**\u003cbr\u003ehttps://youtu.be/Q4GNLhRtZNc | **Exercise #4** |\n| **Week 6** | **Logistic Regression**\u003cbr\u003e- Decision boundary for classification\u003cbr\u003e- Cost function and gradient descent for Logistic Regression\u003cbr\u003e- Multiclass classification | Lecture / Implementation | | **Exercise #5** |\n| **Week 7** | | | | **Midterm Exam** |\n| **Week 8** | **Naïve-Bayes Algorithm**\u003cbr\u003e- Review of conditional probability\u003cbr\u003e- Bayes Rule\u003cbr\u003e- Independent events\u003cbr\u003e- Naïve Bayes for classification | Lecture / Examples | **Visualized Naïve-Bayes:**\u003cbr\u003ehttps://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html | **Exercise #6** |\n| **Week 9** | **Support Vector Machines**\u003cbr\u003e- The Hyperplane\u003cbr\u003e- Kernel method | Lecture / Demo | | **Exercise #7** |\n| **Week 10** | **Decision Trees** | Lecture / Implementation | | **Exercise #8** |\n| **Week 11** | **Ensemble Learning and Random Forests** | Lecture / Hands-on | | **Exercise #9** |\n| **Week 12** | | | | |\n| **Week 13** | | | | **Course Project Submission** |\n\n\n\n# 🏆 **Grading**\n\n### Exercises  (5**0%)**\n\n### Midterm Exam (1**0%)**\n\n###  Project (40%)\n\n\n# 🌌 Google Collab\n\n### **Create your own GitHub Account.**\n\n1. Go to https://research.google.com/colaboratory/\n2. Select **new notebook**\n\n# 💻 Software Requirements\n\n🐍 Download **Miniconda**\n\n- Go to https://docs.conda.io/en/latest/miniconda.html\n- In the latest miniconda installer links, click **the download link** depending on the platform you are using.\n- For macOS and Linux users, choose the link for macOS and Linux.\n- Install the downloaded file.\n\n🐍 Download **Python Extension Pack**\n\n- After installing Microsoft Visual Studio Code, **go to Extensions or press Ctrl+Shift+x**\n- In the Search Box, enter python\n- Look for **Python Extension Pack** in the search result and **click Install.**\n\n# 📚 Readings/References:\n\n### **Book/E-books**\n\n[Géron , Aurélien 2019 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://github.com/yanshengjia/ml-road/blob/master/resources)\n\n[Hands On Machine Learning with Scikit Learn and TensorFlow.pdf](https://github.com/yanshengjia/ml-road/blob/master/resources/Hands%20On%20Machine%20Learning%20with%20Scikit%20Learn%20and%20TensorFlow.pdf)\n\n\n\u003c!-- Always document your changes, pull-request, bugfix, updates, patch notes for this final project. Always use this \"🧊 Flight Booking\" for commiting message for \"pushing code\" or \"Pull-request\"   --\u003e\n# 📫 Changelogs \nChronological list of updates, bug fixes, new features, and other modifications for Machine Learning topic.\n\n### 📦 Version 1.0.0 - July 23, 2025\n**Project Initialization**\n- ✨ Created initial repository structure\n- ✨ Set up project folder \n- ✨ Established ML development workflow \u0026 README.md\n- 🔧 Initial project configuration and setup\n\n🧊 CCMACLRL\n\n\u003c!-- Introduction Pannel button link, it will redirect to the top --\u003e\n\n#### [Back to Table of Content](https://www.youtube.com/watch?v=2gJJzspizFk\u0026list=RDfFqxDrmQLnQ\u0026index=13)\n\n\u003c!-- End point line insert Thanks for visiting enjoy your day, feel free to modify this  --\u003e\n---\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://readme-typing-svg.demolab.com/?lines=Thanks+For+Visiting+Enjoy+Your+Day+~!;\" alt=\"mystreak\"/\u003e\n\u003c/p\u003e\n\n\u003c!-- Siero Miero --\u003e\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPWVjZjA1ZTQ3OGJ0aW80YnkwcjdmNzdzZ2tuMDdpaTZydzV5dTQ3M2VtdXlrd2k0ayZlcD12MV9zdGlja2Vyc19zZWFyY2gmY3Q9cw/iF7FoIWjpHD7E2ndx4/giphy.gif\" width=\"300\"\u003e\n\u003c/div\u003e\n\n\u003c!-- End point line insert Comeback again next time, feel free to modify this  --\u003e\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://readme-typing-svg.demolab.com/?lines=Come+Back+Again+next+time\" alt=\"mystreak\"/\u003e\n\u003c/p\u003e\n\n\u003c/p\u003e\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflexycode%2Fccmaclrl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fflexycode%2Fccmaclrl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflexycode%2Fccmaclrl/lists"}