Introduction to Python for AI Programmers

This repository contains examples and demo code for Udacity's Introduction to Python for AI Programmers lessons. The content is designed to teach object-oriented programming (OOP) principles and Python practices through hands-on exercises and real-world examples. Additionally, the repository includes a Docker Compose configuration to set up a local development environment easily.

Repository Contents

The following directories contain example and exercise files that can be executed in a Jupyter Notebook environment:

• exercise-oop-syntax-practice-part-1: Part 1 of OOP syntax practice exercises.
• exercise-oop-syntax-practice-part-2: Part 2 of OOP syntax practice exercises.
• exercise-code-the-gaussian-class: Exercise for building and using a Gaussian class.
• exercise-code-magic-methods: Exercise focused on understanding and implementing Python magic methods.
• exercise-inheritance-with-clothing: Practice exercise on inheritance using a clothing class example.
• demo-inheritance-probability-distributions: Demo showing inheritance applied to probability distributions.
• exercise-making-a-package-and-pip-installing: Exercise on creating and installing a Python package with pip.

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The following directories contain files meant for more advanced OOP topic exercises that are typically performed in an integrated development environment (IDE):

• 1_instruction_files: Instructional files for advanced OOP topics.
• 2_modularized_code: Examples demonstrating how to modularize Python code.
• 3b_answer_python_package: Solution code for building and structuring a Python package.
• 4a_binomial_package: Example code for implementing a binomial distribution package.
• 4b_answer_binomial_package: Solution code for the binomial distribution package exercise.
• 5_exercise_upload_to_pypi: Exercise files for uploading a Python package to PyPI.

How to Use This Repository

1. Clone the repository to your local machine:

   git clone https://github.com/udacity/ls12011-intro-to-oop.git

2. Navigate to the specific directory that matches your lesson or topic of interest.

3. Follow the instructions within the files to explore the examples, run the code, or complete exercises.

How to Use the Containerized Jupyter Environment (Optional)

1. Ensure Docker and Docker Compose are installed on your system.

2. Clone the repository to your local machine:

   git clone https://github.com/udacity/ls12011-intro-to-oop.git

3. Navigate to the project directory:

   cd ls12011-intro-to-oop

4. Set up the .env file:

   • Create a .env file in the root of the project directory to configure the container environment variables. Below is an example of what the file might include:

   # .env file
   JUPYTER_PORT=8888
   JUPYTER_TOKEN=your_custom_token

   • Replace your_custom_token with a secure token to access Jupyter Notebook.
   • Modify JUPYTER_PORT if you want to use a different port.

5. Build and start the Docker containers:

   docker-compose up --build
   

6. Access the development environment: Open your web browser and navigate to http://localhost:8888/?token=your_custom_token. Use the token in your .env file (JUPYTER_TOKEN) to log in to the Jupyter Notebook interface.

License

This repository is licensed under Udacity's terms. The LICENSE file in the repository provides more details.