AI Learning Project: Cat and Dog Image Classification

This project is designed to help you learn the basics of AI and Machine Learning through a practical image classification task. We'll build a neural network that can distinguish between cats and dogs.

Project Structure

ai_learning_project/
├── data/               # Directory for training and test images
├── model/             # Directory to save the trained model
├── requirements.txt   # Project dependencies
├── train.py          # Script to train the model
└── predict.py        # Script to make predictions

Setup Instructions

1. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Download the dataset:
   • Create a data directory
   • Download the Dogs vs Cats dataset from Kaggle
   • Extract the images into the data directory

Project Components

1. Data Preprocessing: We'll learn how to:

   • Load and preprocess images
   • Split data into training and validation sets
   • Normalize pixel values

2. Model Building: We'll create a CNN that:

   • Uses convolutional layers for feature extraction
   • Implements pooling layers for dimensionality reduction
   • Uses dense layers for classification

3. Training: We'll learn about:

   • Model compilation
   • Training loops
   • Validation
   • Early stopping

4. Evaluation: We'll explore:

   • Model accuracy
   • Confusion matrices
   • Learning curves

Learning Outcomes

By completing this project, you'll understand:

• Basic concepts of neural networks
• Image preprocessing techniques
• Model training and evaluation
• Common challenges in ML projects
• Best practices for model development

Next Steps

After completing this project, you can:

1. Try different architectures
2. Experiment with data augmentation
3. Implement transfer learning
4. Add more classes to the classifier
5. Deploy the model as a web application