https://github.com/flexycode/ccmaclrl
🤖 This repository is intended for our Machine Learning CCMACLRL COM231ML by Professor Elizer Ponio Jr
https://github.com/flexycode/ccmaclrl
artificial-intelligence linnear-regression machine-learning machine-learning-algorithms python random-forest scikit-learn supervised-learning tensorflow
Last synced: about 1 month ago
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🤖 This repository is intended for our Machine Learning CCMACLRL COM231ML by Professor Elizer Ponio Jr
- Host: GitHub
- URL: https://github.com/flexycode/ccmaclrl
- Owner: flexycode
- License: mit
- Created: 2025-08-04T05:57:55.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-10T12:06:30.000Z (10 months ago)
- Last Synced: 2025-08-10T14:18:30.157Z (10 months ago)
- Topics: artificial-intelligence, linnear-regression, machine-learning, machine-learning-algorithms, python, random-forest, scikit-learn, supervised-learning, tensorflow
- Homepage:
- Size: 3.91 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 💫 Introduction to Machine Learning
### Name: [Jay Arre Talosig](https://www.youtube.com/watch?v=-er2ruCgzjg&list=RDfFqxDrmQLnQ&index=4)
### Subject & Section: [CCMACLRL - COM231ML](https://www.youtube.com/watch?v=fFqxDrmQLnQ&list=RDfFqxDrmQLnQ&start_radio=1)
### Schedule: [TUE 11:00AM - 01:40 PM VR09CCIT - FRI 11:00AM - 03:00 PM 408 MB](https://www.youtube.com/watch?v=dL7Vn7hJDAk&list=RDdL7Vn7hJDAk&start_radio=1)
### Professor: [Elizer Ponio Jr](https://github.com/robitussin/)
### No. of Units: [3 Units](https://www.youtube.com/watch?v=UVJSA2N39NU&list=RDUVJSA2N39NU&start_radio=1)
### Prerequisite: [Python & Common Sense](https://www.pornhub.com/)
### Subject Repo Link: [Professor Elizer Machine Learning Repo](https://github.com/robitussin/CCMACLRL)
### Project Link: [CCMACLRL_COM231_PROJECT](https://github.com/flexycode/CCMACLRL_COM231_PROJECT)
### Lab Activity Link: [CCMACLRL_EXERCISES_COM231ML](https://github.com/flexycode/CCMACLRL_EXERCISES_COM231ML)
# 📜 Course Description
This 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.
# Pre-requisites for this class
- **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.
- **College Calculus, Linear Algebra**. You should be comfortable taking derivatives and understanding matrix vector operations and notation.
- **Basic Probability and Statistics.** You should know basics of probabilities, Gaussian distributions, mean, standard deviation, etc.
# **🚀** Learning Outcomes
By the end of the class students should be able to:
- Demonstrate sufficient knowledge in using various machine learning libraries and tools.
- Understand foundational algorithms used for model building for machine learning.
- 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.
# 📅 Course Outline and Timeframe
| Week | Topic | Mode of Delivery | Readings/Videos | Events |
|------|-------|------------------|-----------------|--------|
| **Week 1** | **Class Orientation**
- Course Syllabus
- Expectations for Online Classes / Class Setup
- Grading and Deadlines
- CAM
**Class Setup for Machine Learning**
- Introduction to Google Colab and Jupyter Notebook
- Basic Python Tutorial | Online Lecture / Tutorial | **Google Colaboratory:** https://colab.research.google.com/
**Get Started with Google Colab:**
https://www.youtube.com/watch?v=inN8seMm7UI | |
| **Week 2** | **Introduction to the Course**
- History of AI and Machine Learning
- Artificial Intelligence vs. Machine Learning
- Taxonomy of ML
- Goals and Limitations of ML
- Real world applications | Lecture / Discussion | **What is Artificial Intelligence?**
https://youtu.be/mJeNghZXtMo | **Exercise #1** |
| **Week 3** | **K Nearest Neighbors**
- Definition and Intuition
- Hyperparameter (k)
- Classification using k-NN
- Applications | Lecture / Demo | **K-Nearest Neighbors Demo:**
http://vision.stanford.edu/teaching/cs231n-demos/knn/ | **Exercise #2** |
| **Week 4** | **Simple Linear Regression**
- Equation of a line
- Cost function intuition
- Parameters
- Implementation | Lecture / Hands-on | **An Introduction to Linear Regression Analysis:**
https://www.youtube.com/watch?v=NUXdtN1W1FE | **Exercise #3** |
| **Week 5** | **Multiple Linear Regression**
- Model representation
- Gradient descent for multiple variables
- Feature scaling
- Normal equation
- Application | Lecture / Coding | **Linear Regression with Multiple Variables:**
https://youtu.be/Q4GNLhRtZNc | **Exercise #4** |
| **Week 6** | **Logistic Regression**
- Decision boundary for classification
- Cost function and gradient descent for Logistic Regression
- Multiclass classification | Lecture / Implementation | | **Exercise #5** |
| **Week 7** | | | | **Midterm Exam** |
| **Week 8** | **Naïve-Bayes Algorithm**
- Review of conditional probability
- Bayes Rule
- Independent events
- Naïve Bayes for classification | Lecture / Examples | **Visualized Naïve-Bayes:**
https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html | **Exercise #6** |
| **Week 9** | **Support Vector Machines**
- The Hyperplane
- Kernel method | Lecture / Demo | | **Exercise #7** |
| **Week 10** | **Decision Trees** | Lecture / Implementation | | **Exercise #8** |
| **Week 11** | **Ensemble Learning and Random Forests** | Lecture / Hands-on | | **Exercise #9** |
| **Week 12** | | | | |
| **Week 13** | | | | **Course Project Submission** |
# 🏆 **Grading**
### Exercises (5**0%)**
### Midterm Exam (1**0%)**
### Project (40%)
# 🌌 Google Collab
### **Create your own GitHub Account.**
1. Go to https://research.google.com/colaboratory/
2. Select **new notebook**
# 💻 Software Requirements
🐍 Download **Miniconda**
- Go to https://docs.conda.io/en/latest/miniconda.html
- In the latest miniconda installer links, click **the download link** depending on the platform you are using.
- For macOS and Linux users, choose the link for macOS and Linux.
- Install the downloaded file.
🐍 Download **Python Extension Pack**
- After installing Microsoft Visual Studio Code, **go to Extensions or press Ctrl+Shift+x**
- In the Search Box, enter python
- Look for **Python Extension Pack** in the search result and **click Install.**
# 📚 Readings/References:
### **Book/E-books**
[Géron , Aurélien 2019 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://github.com/yanshengjia/ml-road/blob/master/resources)
[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)
# 📫 Changelogs
Chronological list of updates, bug fixes, new features, and other modifications for Machine Learning topic.
### 📦 Version 1.0.0 - July 23, 2025
**Project Initialization**
- ✨ Created initial repository structure
- ✨ Set up project folder
- ✨ Established ML development workflow & README.md
- 🔧 Initial project configuration and setup
🧊 CCMACLRL
#### [Back to Table of Content](https://www.youtube.com/watch?v=2gJJzspizFk&list=RDfFqxDrmQLnQ&index=13)
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