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Getting Started with AI Programming with Python

Welcome! This repository contains coursework from Udacity's AI Programming with Python Nanodegree. It includes example notebooks, small projects, and a final image classifier project. Use this guide to navigate the contents and run the main code.

Repository Structure

  • 1-introduction-to-ai-programming/ – a short introduction document for the course.
  • 2-intro-to-python/ – notebooks and exercises covering Python fundamentals. Includes a small dog image classifier project (lesson-6-classifying-images/workspace/).
  • 3-numpy-pandas-matplotlib/ – notebooks showing how to use NumPy, Pandas, and Matplotlib.
  • 5-neural-networks/dl-with-pytorch/ – notebooks and helper code for learning PyTorch and building neural networks.
  • 6-create-your-own-classifier-project/ – final project with scripts (train.py, predict.py) for training and using an image classifier.

Key Scripts

  • train.py – trains a convolutional neural network using command line arguments to set hyperparameters.
  • predict.py – loads a trained checkpoint and predicts the top classes for a given image.

Next Steps

  1. Explore the introductory notebooks to refresh your Python and data analysis skills.
  2. Run setup-dataset.bash in the final project folder to download the flower dataset.
  3. Use train.py to train a model, then run predict.py to classify new images.

Happy learning!