Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn how to use machine learning, specifically support vector regression (SVR), to predict the quality of additive manufacturing parts in this comprehensive 55-minute tutorial. Explore the challenges of 3D printing accuracy and discover how to train an SVR model using real factory data to predict part dimensions based on design geometry and manufacturing parameters. Master techniques like grid search hyperparameter tuning and nested cross-validation to improve model performance. Compare SVR with other algorithms such as k-nearest neighbors (KNN) and evaluate their computational cost and predictive accuracy. Gain hands-on experience using Jupyter Notebooks on nanoHUB to apply these concepts in practice.