<ul><li>The tutorial on Eigenfaces and machine learning was taken on as a side project by someone still learning ML.</li><li>The project was done without using scikit-learn to maintain a beginner-friendly approach.</li><li>Google Colab was preferred for its suitability for such projects.</li><li>The dataset used was 'AT&T Database of Faces' from Kaggle.</li><li>Libraries used included os, cv2 (OpenCV), and numpy for file system interaction and image processing.</li><li>Principal Component Analysis (PCA) was explained as a method for dimensionality reduction.</li><li>Important steps included finding eigenfaces, centering images, computing covariance matrix, and projecting faces into eigenface space.</li><li>The recognize_face() function was replaced with a new function for improved performance.</li><li>The predict_face() function was detailed, highlighting how face recognition works in eigenface space.</li><li>A threshold was explained as a confidence level for face recognition to prevent false positives.</li><li>The tutorial concluded with visualizing the results and uploading a personal image for testing.</li></ul>

How Eigenfaces Got Me Hooked on Machine Learning

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