Introduction to Image Classification

Image classification is one of the most common applications of Artificial Intelligence (AI) and Computer Vision. It enables computers to identify and classify images into predefined categories based on their visual features. Instead of manually identifying objects in an image, a trained deep learning model can automatically recognize and predict the correct class.

In this project, we will build an Image Classification System using TensorFlow and Keras. We will use the CIFAR-10 dataset to train both an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN). By comparing the performance of these models, learners will understand why CNNs are widely used for image classification tasks.

By the end of this project, you will be able to preprocess image data, train deep learning models, evaluate their performance, and classify new images using Python.

Data Science is the process of collecting, analyzing, and interpreting data to solve real-world problems. It combines programming, mathematics, statistics, and machine learning to extract useful information from data.

The typical Data Science workflow includes:

• Data Collection
• Data Preprocessing
• Data Analysis
• Model Building
• Model Evaluation
• Prediction

In this project, we use Data Science techniques to analyze image data and build a deep learning model that can classify images into different categories.

Key Features of Data Science

• Collects and processes data
• Identifies patterns and trends
• Builds intelligent prediction models
• Supports data-driven decision making
• Solves real-world problems using AI