Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Classifying Im

The video tutorial covers the process of working with a dataset containing 10 categories of images, including airplanes, automobiles, and more. It explains how to display images using matplotlib, normalize data by scaling pixel values, and create a Convolutional Neural Network (CNN) model. The tutorial also discusses training the model over 10 epochs, evaluating its performance, and identifying areas for improvement. The model achieves a 62% accuracy rate, with suggestions for further tuning and optimization.

What are the 10 categories mentioned in the data set?

How does the teacher plan to display the images from the training data set?

What challenges are mentioned regarding the quality of the images?

What is the purpose of normalizing the image data?

Describe the structure of the CNN model mentioned in the text.

What was the test accuracy achieved after training the model?

What are some reasons given for the model's incorrect predictions?