Python Programming Course - Saudi Arabia

Introduction to Python & Setup:

1. Installing Python and Anaconda/Jupyter Notebook.
2. Writing your first "Hello, World!" program.

Variables and Data Types:

1. Working with Strings, Integers, Floats, and Booleans.
2. Type casting (converting one data type to another).

Control Flow & Logic:

1. if, elif, and else statements.
2. Loops: for loops and while loops for repeating tasks.

Data Structures (Storing Data):

1. Lists, Tuples, Dictionaries, and Sets.

Functions & Modules:

1. How to write reusable code blocks (def).
2. Importing built-in Python modules.

Module 2: Data Handling for AI (The Essential Libraries)

NumPy (Numerical Python):

1. Introduction to arrays and matrices (how AI sees data).
2. Basic mathematical operations on arrays.

Pandas (Data Analysis):

1. Loading CSV/Excel files into Python using DataFrames.
2. Cleaning data (handling missing values, filtering rows/columns).

Matplotlib & Seaborn (Data Visualization):

1. Creating line graphs, bar charts, scatter plots, and histograms.

Module 3: Introduction to Artificial Intelligence & Machine Learning

AI vs. Machine Learning vs. Deep Learning:

1. Understanding the differences and the big picture.

Supervised Learning (Predicting Outcomes):

1. Regression: Predicting numbers (e.g., House Price Prediction).
2. Classification: Predicting categories (e.g., Email Spam vs. Not Spam).
3. Unsupervised Learning (Finding Patterns):
4. Clustering: Grouping similar data together (e.g., Customer Segmentation).
5. Model Evaluation: How to check if your AI model is accurate (Train/Test split, Accuracy scores).

Module 4: Generative AI & API Integration (The Modern AI)

Introduction to Large Language Models (LLMs):

1. What are LLMs and how do they work?

Working with AI APIs:

1. Setting up an OpenAI or Google Gemini API key.
2. Writing Python code to send prompts and get AI responses.

Basic Prompt Engineering via Code:

1. Automating tasks like text summarization, translation, or chatbot replies.

Module 5: Capstone Projects (Hands-on Practice)

1. Project 1: A Python-based automated Student Grading System (Pure Python).
2. Project 2: A Data Analysis Dashboard visualizing real-world Covid or Sales data (Pandas & Matplotlib).
3. Project 3: A Machine Learning model that predicts whether a student will pass or fail based on study hours (Scikit-Learn).
4. Project 4: A Smart AI Chatbot assistant built using Python and an LLM API.

Basic to Advance Python Programming Course Outline

Introduction to Python Programming

1. Overview of Python
2. Advantages of Python
3. Setting up the Python Environment
4. Understanding Python Variables
5. Basic Data Types in Python

Operators and Expressions

1. Arithmetic Operators
2. Comparison Operators
3. Logical Operators
4. Assignment Operators
5. Bitwise Operators

Flow Control Statements

1. If Statements
2. For Loops
3. While Loops
4. Break and Continue
5. Pass Statement

Functions

1. Defining Functions
2. Calling Functions
3. Return Statement
4. Function Arguments
5. Recursion

Data Structures

1. Lists
2. Tuples
3. Dictionaries
4. Sets
5. Indexing and Slicing

Modules and Packages

1. Importing Modules
2. Creating Custom Modules
3. Using Third-Party Packages
4. Installing Packages

File Handling

1. Opening and Closing Files
2. Reading and Writing Files
3. Exception Handling
4. Working with Directories

Object-Oriented Programming

1. Understanding Classes and Objects
2. Defining Classes
3. Constructors and Destructors
4. Inheritance
5. Overloading and Overriding

Advanced Python Concepts

1. Generators
2. Decorators
3. Iterators
4. Context Managers

Web Development with Python

1. Introduction to Flask
2. Setting up the Flask Environment
3. Creating a Flask Application
4. Adding Templates and Static Files
5. Deploying the Flask Application

Data Science with Python

1. Introduction to NumPy
2. Understanding Arrays
3. Mathematical Operations
4. Introduction to Pandas
5. Data Analysis and Visualization

Machine Learning with Python

1. Introduction to scikit-learn
2. Regression Analysis
3. Classification
4. Clustering
5. Dimensionality Reduction

Final Project

1. Introduction to the Final Project
2. Project Requirements
3. Submission Guidelines
4. Evaluation Criteria

Conclusion

1. Recap of Python Programming
2. Future Opportunities with Python
3. Certificate of Completion