Skip to content

Latest commit

 

History

History
109 lines (74 loc) · 5.02 KB

README.md

File metadata and controls

109 lines (74 loc) · 5.02 KB
Raw

Look to notebooks/classification.ipynb for a full, general tutorial on machine learning classification.

1. Introduction

The models developed in classification.ipynb explore classifying numbers from the MNIST database, a large database of handwritten digits that are commonly used for training various image processing systems. The database has been used so often it is sometimes called the "hello world" of machine learning classification.

2. Figures Created in the Tutorial

Data science insights are derived through visualizations. Below, you can see some of the interesting figures you'll be able to generate from the notebook. I hope you enjoy the tutorial. 🌞

3. References

  1. Géron, A. (2019, October 15). Classification. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.) (pp. 85-108). O’Reilly Media.
  2. Aurelion Geron's Personal GitHub Notebook on Classification
  3. The MNIST Database's Wikipedia Page
  4. Professor Yann LeCun's Breakdown of Algorithms Applied to the MNIST Dataset

4. Appendix: Setup for New Developers

If you are fairly new to Python programming, I'd recommend the setting up this project by following these steps. If you want more in-depth knowledge about environment setup, I'd recommend you read my tutorial on interfacing with the computer like a software developer.

  1. Download and install VS Code.

  2. Install Python 3.12.4 (☑️ Add python.exe to PATH if you have no other Python versions installed).

  3. Install Git bash.

  4. Open VS Code.

  5. Press F1, and in the command palette, search for Terminal: Select Default Profile and set Git bash as the default terminal.

  6. Start a new terminal with Ctrl + `.

  7. Clone this repository to a directory where you like to store your coding projects.

  8. Open this repository (i.e. the ml-classification folder) as the current workspace folder with Ctrl + K Ctrl + O.

  9. Make sure the terminal path points to the qsnake folder, and if it doesn't, navigate there via cd <path_to_ml_classification_folder>. You can confirm you're in the right spot with quick ls -la command.

  10. From the terminal, run pip install virtualenv to install the virtualenv module.

  11. Run python -m virtualenv <myenvname> --python=python3.12.4 to create a virtual environment that runs on Python 3.12.4.

  12. Activate the virtual environment with source <myenvname>/Scripts/activate.

  13. You should see (<myenvname>) two lines above the terminal input line when the environment is active.

  14. Press F1 to open VS Code's command palette, then search for Python: Select Interpreter and select Python 3.12.4 64-bit ('<myenvname>':venv).

  15. Run pip install -r requirements.txt to install all dependencies on your activated virtual environment.

  16. Navigate to notebooks/classification.ipynb and read through the tutorial, running the cells as you go along if you'd like.