Image Classification in Python with TensorFlow | Machine Learning

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In today's YouTube coding tutorial, we will perform Image Classification in Python with TensorFlow.
This machine learning model for image classification helps us to classify images based on the most popular dataset CIFAR-10 dataset (Canadian Institute for Advanced Research)

Objective:
The objective behind building this machine learning model is to classify images based on the CIFAR-10 dataset, which is the most popular dataset which is used to train machine learning and computer vision algorithms.

Image classification applications are used in many areas, such as medical imaging, object identification in satellite images, traffic control systems, brake light detection, machine vision, and more. To find more real-world image classification applications, check out our extensive list of AI vision applications.

Requirements:
1. Python with all the necessary libraries installed
2. Jupyter Notebook
3. CIFAR-10 dataset

Explanation of code:
Initially, we loaded our dataset of CIFAR-10, then, accordingly, we split the data into the training and testing phases.
Then we trained artificial and convolutional neural networks, and then we compared the results based on accuracy.
After comparing the results of both neural networks, we found that the convolutional neural network is the best for image classification, with an accuracy of 70 percent.
Hence we have finally used a convolutional neural network and predicted the results.

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