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?

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

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What challenges are mentioned regarding the quality of the images?

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What is the purpose of normalizing the image data?

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

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What was the test accuracy achieved after training the model?

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

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