Machine Learning Projects with PyTorch This repository contains a collection of machine learning projects developed using PyTorch. Each project focuses on different aspects of machine learning, ranging from image classification to customer segmentation.

📁 Project Structure cifar10_image_classifier: Implementation of a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. This project demonstrates the process of building, training, and evaluating a deep learning model for image classification tasks.

identify_customer_segment: Application of unsupervised learning techniques, such as clustering, to segment customers based on purchasing behavior. This project showcases data preprocessing, feature selection, and the use of clustering algorithms to identify distinct customer groups.

🧠 Key Concepts Deep Learning with PyTorch: Utilizing PyTorch's dynamic computation graph and tensor operations to build and train neural networks.

Image Classification: Techniques for classifying images into predefined categories using CNNs.

Unsupervised Learning: Applying clustering algorithms to uncover hidden patterns in data without labeled responses.

Data Preprocessing: Cleaning and transforming raw data into a suitable format for machine learning models. GitHub

🛠️ Technologies Used Programming Language: Python

Libraries and Frameworks: PyTorch, NumPy, Pandas, Matplotlib, Scikit-learn

Tools: Jupyter Notebook Medium

📷 Sample Visualizations

Diagram illustrating the typical workflow of building and training models using PyTorch.

📚 References PyTorch Documentation

Scikit-learn Documentation

CIFAR-10 Dataset