• Image compression using K-MeansOpen in HTMLOpen in Colab.
• Change the image’s size from (height, weight, channels) to (height x weight, channels)
• Reduce the image’s quality using smaller number of clusters.
• Example to understand the idea of PCAOpen in HTMLOpen in Colab.
• Plot points with 2 lines which are corresponding to 2 eigenvectors.
• Plot & choose Principal Components.
• An example of choosing n_components $K$.
• Visualization hand-written digits (the case of all digits and the case of only 2 digits – 1 & 8).
• Using SVM to classifier data in the case of 1 & 8 and visualize the decision boundaries.
• Image compression using PCAOpen in HTMLOpen in Colab.
• When input is an image, the values of adjacent pixels are highly correlated.
• Import images from scipy and Google Drive or Github (with git).
• Compress grayscale images and colored ones.
• Plot a grayscale version of a colorful images.
• Save output to file (Google Drive).
• Fix warning Lossy conversion from float64 to uint8. Range […,…]. Convert image to uint8 prior to saving to suppress this warning.
• Fix warning Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
• Calculate a size (in KB) of a image file.
• PCA without scikit-learnOpen in HTMLOpen in Colab.
• Face Recognition using SVMOpen in HTMLOpen in Colab.
• Using PCA to extract 150 fundamental components to feed into our SVG classifier.
• Grid search cross-validation to explore combinations of parameters (gamma and C).
• Classification report: precision, recall, f1-score, support.
• Confusion matrix.
• An example of using pipeline.
• XOR problem using SVM to see the effect of gamma and C in the case of using RBF kernel – Open in HTMLOpen in Colab.