Python for AI Students

A condensed, modular Python curriculum designed for AI students. Eight level-ranked modules covering Python foundations through data analysis, visualization, NumPy, and introductory machine learning.

Course Structure

Module	Topics	Notebook	Reference PDF	Challenges
Module 1	Syntax, Variables, Types, Operators, Control Flow, Data Structures, Functions, Scope, List Comprehensions, Lambda	Module_1_Python_Foundations.ipynb	Module_1_Reference.pdf	Module_1_Challenges.ipynb
Module 2	Classes, Inheritance, File I/O, Exceptions, NumPy Arrays, Slicing, Broadcasting	Module_2_OOP_Files_NumPy.ipynb	Module_2_Reference.pdf	Module_2_Challenges.ipynb
Module 3	Pandas, Data Cleaning, Grouping, Matplotlib, End-to-End Mini-Project	Module_3_Pandas_Matplotlib_MiniProject.ipynb	Module_3_Reference.pdf	Module_3_Challenges.ipynb
Module 4	NumPy Review and Practice	Module_4_NumPy_Tutorial.ipynb	—	—
Module 5	Linear Regression Tutorial	Module_5_Linear_Regression_Tutorial.ipynb	—	—
Module 6	Logistic Regression Lab	Module_6_Logistic_Regression_Lab.ipynb	—	—
Module 7	Heart Disease Classification	Module_7_Heart_Disease_Classification.ipynb	—	—
Module 8	Fuel Consumption / CO2 Regression	Module_8_Fuel_Consumption_CO2_Regression.ipynb	—	—

Data

Sample datasets for exercises and mini-projects:

• data/students.csv - 50 student records with GPA, attendance, department
• data/sales.csv - 100 sales transactions across categories and regions
• data/FuelConsumptionCo2.csv - fuel consumption and CO2 emissions data
• data/ex1data1.txt - one-variable linear regression practice data
• data/ex1data2.txt - multivariable linear regression practice data
• data/heart_cleveland.csv - heart disease classification data

Resources

Official Documentation

• Python.org Downloads
• Python 3 Documentation
• Python Tutorial

Tutorials

• After Hours Programming - Python
• Learn Python in 10 Minutes
• Python for Scientists
• Python Beginners Guide

Local Cheat Sheets

The resources/ directory contains 17 reference PDFs organized by topic and module relevance:

Python Fundamentals (Modules 1–2)

Cheat Sheet	Relevant Modules
python-tutorial-afterhours.pdf	Module 1 — Core syntax and concepts
python-keywords-cheatsheet.pdf	Module 1 — Keywords and reserved words
python-data-structures-cheatsheet.pdf	Module 1 — Lists, dicts, tuples, sets
python-functions-tricks-cheatsheet.pdf	Module 1 — Functions, lambdas, list comprehensions
python-dense-cheatsheet-finxter.pdf	Module 1–2 — Quick-reference for all basics
python-complex-types-cheatsheet-finxter.pdf	Module 1 — Advanced data types
python-cheatsheet.pdf	Module 1–2 — General Python reference

Object-Oriented Programming (Module 2)

Cheat Sheet	Relevant Modules
python-classes-cheatsheet.pdf	Module 2 — Classes, inheritance, OOP

NumPy & Arrays (Modules 2, 4)

Cheat Sheet	Relevant Modules
numpy-cheat-sheet-11.pdf	Module 2, 4 — Array operations and broadcasting
numpy-cheatsheet-1.pdf	Module 2, 4 — NumPy essentials
numpy-cheatsheet-2.pdf	Module 2, 4 — Array creation and indexing
numpy-cheatsheet.pdf	Module 2, 4 — NumPy quick reference
numpy-python-cheatsheet.pdf	Module 2, 4 — Full NumPy API overview

Pandas & Matplotlib (Module 3)

Cheat Sheet	Relevant Modules
pandas-cheatsheet.pdf	Module 3 — DataFrames, grouping, cleaning
matplotlib-cheatsheet.pdf	Module 3 — Plotting and visualization

Linear Algebra & Machine Learning (Modules 5–8)

Cheat Sheet	Relevant Modules
matrix-cheatsheet-table.pdf	Module 5, 8 — Matrix operations for regression

General / Interview Prep

Cheat Sheet	Relevant Modules
python-coding-interview-questions.pdf	All modules — Practice problems and solutions

Interactive Tools

• Python Tutor - Visualize code execution step by step
• Replit - Online Python editor

Arabic Resources

• Python Arabic Language Resources

Setup (Using uv)

This project uses uv for fast Python package management and virtual environments.

1. Install uv

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

For other methods (pip, Homebrew, etc.), see the official uv installation guide.

2. Create a Virtual Environment

From the project root (where this README.md is):

uv venv --python 3.14

This creates a .venv/ directory with a self-contained Python interpreter.

3. Install Dependencies

uv pip install numpy pandas matplotlib scikit-learn nbconvert nbformat ipykernel

What each package is for:

Package	Purpose
numpy	Numerical computing (arrays, matrices, math operations)
pandas	Data manipulation and analysis (DataFrames)
matplotlib	Data visualization and plotting
scikit-learn	Machine learning utilities (used in regression and classification modules)
nbconvert	Convert notebooks to other formats
nbformat	Read/write Jupyter notebook files
ipykernel	Jupyter kernel for running notebooks in VS Code/Jupyter

4. Register the Jupyter Kernel

So VS Code and Jupyter can find this environment:

.venv/bin/python -m ipykernel install --user --name ec-ai-python

Windows users: Use .venv\Scripts\python -m ipykernel install --user --name ec-ai-python

Then in VS Code, click the kernel selector (top-right of a notebook) and choose "ec-ai-python".

5. Verify Everything Works

Run the notebooks from the modules/ directory in VS Code or Jupyter. The data files are referenced as ../data/... (relative to the modules/ folder).

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How to Use

1. Open the notebooks in Jupyter, VS Code, or Google Colab
2. Each module notebook covers concepts with examples and exercises
3. Each challenges notebook contains programming problems for practice
4. Reference the PDF documents for detailed concept explanations
5. Modules 3, 5, 7, and 8 use the provided data files in the data/ directory

Credits

Curriculum designed for the EC AI Student Course. Based on the After Hours Programming Python tutorial and supplemented with modern Python practices.