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The Core Knowledge Domains in Coding for AI

HTML

HTML

HTML I
HTML & JavaScript

1. Tables

2. Column Span & Row Span

3. CSS

4. FTP uploads and Publish Websites

HTML II
HTML & JavaScript

1. Images in Tables

2. Audio & Video

3. Hyperlinks & Displays

4. Displays with buttons

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Each course consists of four lessons. Every lesson begins with two video tutorials followed by practical exercises. Sample codes for the exercises will be provided to assist you. After completing the lessons, you will receive two sets of homework assignments to reinforce your learning. You can submit your completed homework, and the instructor will review your code and provide feedback.

HTML III
HTML & JavaScript

1. Inputs & Forms

2. Radio Inputs

3. Form & Operation

4. Form Publishing

Python

Python

Python I
Basic Programming

1. Arrays

2. Functions

3. Dictionary

4. MySQL

Python II
Basic Programming

1. Geometry & HandTracking

2. Geometry Colours

3. Fingers in Geometry

4. Data

Python III
Basic Programming

1. Numpy & Pandas

2. PyTorch

3. Scikit-Learn

4. TensorFlow

AI Agents

AI Agents

AI Agents I
Access Database 

1. Introduction to LLM

2. LangGraph

3. Function Calling

4. AI Agent & Database

AI Agents II
Access Database 

1. AI Agent-Registration System

2. AI Agent-Exam Results Enquiry

3. Teachers' Lesson Arrangement

4. Teachers' Auto-Marking agent

OpenCV

OpenCV

OpenCV I
JS Library

1. Optical Character Recognition (RegEx)

2. OCR & MySQL

3. Detect and Count The Number of People

4. Emotion Detection

OpenCV II
Python Library 

1. Voice to Text

2. Face Recognition

3. Attendance System with Face Recognition

4. Parking System

MySQL, Frontend (FE) & Backend(BE)

MySQL FE & BE I
with Linux Server

1. MySQL Database & Table

2. Add data into MYSQL on HTML

3. Retrieve data on HTML

4. Retrieve data  and Functions 

Database, Frondend & Backend

MySQL FE&BE II
Access Database 

1. Add & Retrieve Data

2. Delete Data

3. Purchasing System: Retrieve and Delete

4. Inventory System: Retrieve, Add & Delete

ML

Machine Learning
Data Structure

1. Data Processing

2. Model Training- Establish Joblib

3. Model Display in HTML

4. Linear Regression Model

ML

Machine Learning
Data Structure

1. Logistic Regression Model

2. Decision Trees

3. Case I - Sales Prediction

4. Case II- Student Engagement Prediction

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