Project - Artificial Intelligence - ثالث ثانوي

1 Project Not every dataset responds the same to training with all the classification algorithms. In order to achieve the best results for your dataset, you have to experiment with different algorithms. The Python Sklearn library offers a variety of algorithms you can try, including the ones below: > from sklearn.ensemble.forest import Random ForestClassifier > from sklearn.naive_bayes import GaussianNB > from sklearn.svm import SVC Use the training set of the animal faces to train a model that achieves the highest possible accuracy on the testing set. 2 Replace the SGDClassifier library with each of the algorithms mentioned above (Random ForestClassifier, Gaussian NB, SVC) and try to find the best one. 3 Re-run your notebook after each replacement to compute the accuracy of each new model that you try. 4 Create a report that compares the accuracy of all the models that you tried and identifies the one that achieved the best accuracy. وزارة التعليم Ministry of Education 248 2024-1446

Wrap up Now you have learned to: > Prepare images for recognition. > Use libraries and functions to create supervised learning models to classify images. > Describe how a neural network is structured. > Use libraries and functions to create unsupervised learning models to cluster images. > Create images by providing a text prompt. > Fill missing fragments of an image with realistic data. KEY TERMS Computer vision Convolutional Neural Network (CNN) Diffusion Model Feature Engineering Feature Selection Generative Adversarial Network (GAN) Image Recognition Histogram of Oriented Gradients (HOG) Image Generation Image Preprocessing Network Layer Stable Diffusion Standard Scaling Visual Data Ministry of Education 2024-1446 249