This project aims to classify the breed of a dog from an image using deep learning techniques. It leverages the MobileNetV2 architecture to build an efficient and accurate model for dog breed identification.

• Introduction
• Dataset
• Model
• Acknowledgements

Dog breed identification is a challenging problem due to the subtle differences between breeds. This project focuses on building a predictive model using deep learning to accurately identify the breed of a dog from an image. The goal is to create a reliable classifier that can assist in various applications, such as pet adoption, veterinary services, and more.

The dataset used in this project includes images of various dog breeds. Each image is labeled with the corresponding breed name. The dataset is divided into training, validation, and test sets. you can download the dataset here https://www.kaggle.com/c/dog-breed-identification/data.

The project uses the MobileNetV2 architecture, a lightweight deep learning model designed for mobile and embedded vision applications. The main steps include:

1. Data Preprocessing: Preparing the images and labels for training.
2. Model Training: Training the MobileNetV2 model on the dataset using transfer learning.
3. Evaluation: Evaluating the model's performance on the test set to ensure accuracy and robustness.

To run this project, you need to have Python and the following libraries installed:

• TensorFlow
• Keras
• NumPy
• Pandas
• Matplotlib

This notebook was created based on the course Complete A.I. & Machine Learning, Data Science Bootcamp by Andrei Neagoie and Daniel Bourke. The course provided a comprehensive introduction to machine learning concepts and practical implementations using Python and Scikit-Learn.

Thank you to the instructor for the valuable lessons and guidance.