Step by step tutorial on how to recognize cats in a list of images using Machine Learning and AI in Python

I’ll guide you through the process of recognizing cats in a list of images using machine learning and AI in Python. We’ll be using a popular deep learning framework called TensorFlow and its high-level API called Keras. Let’s get started!

Step 1: Set up the environment Before we begin, make sure you have Python installed on your system. You’ll also need to install the following libraries:

• TensorFlow: pip install tensorflow
• Keras: pip install keras
• Matplotlib: pip install matplotlib
• Numpy: pip install numpy

Step 2: Collect cat images To recognize cats, you’ll need a dataset of cat images for training the model. You can either collect the images manually or download a pre-existing dataset. Popular cat image datasets include the Cat vs Dog dataset and the Oxford-IIIT Pet Dataset. For this tutorial, let’s assume you have a folder called ‘cat_images’ containing cat images.

Step 3: Preprocess the data Before training the model, we need to preprocess the images. This involves resizing the images, normalizing pixel values, and splitting the data into training and validation sets.

import os
import cv2
import numpy as np

image_dir = 'cat_images'
image_size = (150, 150)  # Resize images to 150x150 pixels

def preprocess_images(image_dir):
    images = []
    labels = []

    # Iterate over each image in the directory
    for filename in os.listdir(image_dir):
        if filename.endswith('.jpg') or filename.endswith('.png'):
            image_path = os.path.join(image_dir, filename)
            label = 1  # 1 represents cat

            # Read and resize the image
            image = cv2.imread(image_path)
            image = cv2.resize(image, image_size)
            image = image.astype('float32') / 255.0  # Normalize pixel values

            images.append(image)
            labels.append(label)

    images = np.array(images)
    labels = np.array(labels)

    return images, labels

# Preprocess the images
images, labels = preprocess_images(image_dir)

# Split the data into training and validation sets
from sklearn.model_selection import train_test_split
train_images…