Image Classification with Scikit-Learn

In this tutorial, we will learn how to perform image classification using Scikit-Learn, a popular machine learning library in Python.

Step 1: Load the Dataset

First, we need to load a dataset of images that we want to classify. For this tutorial, we will use the well-known MNIST dataset, which contains images of handwritten digits.

Step 2: Preprocess the Data

Next, we need to preprocess the data by normalizing the pixel values and flattening the images into a single vector.

Step 3: Train the Classifier

Now, we can train a classifier on the preprocessed data. In this tutorial, we will use a Support Vector Machine (SVM) classifier from Scikit-Learn.

Step 4: Evaluate the Classifier

After training the classifier, we need to evaluate its performance on a test set of images. We can calculate metrics such as accuracy, precision, recall, and F1 score to assess the performance of the classifier.

Step 5: Make Predictions

Finally, we can use the trained classifier to make predictions on new images. We can visualize the predicted labels and compare them to the ground truth labels to assess the accuracy of the classifier.

That’s it! We have successfully performed image classification using Scikit-Learn in Python. Feel free to experiment with different classifiers and datasets to further enhance your understanding of machine learning.