Explore LIME (Local Interpretable Model-agnostic Explanations) for explaining, trusting, and validating predictions from any machine learning model in this hands-on tutorial. Learn to implement LIME in your ML pipeline through two Jupyter notebooks: one demonstrating LIME explanations with Inception V3 image classification, and another showcasing custom LIME implementation. Discover how to create model explanations for supervised predictions, compare different models, and gain insights into the decision-making process of various algorithms. Dive into topics such as surrogate models, LIME properties, image and tabular data classification explanations, and step-by-step custom explanations using popular models like Inception V3, EfficientNet, and ResNet50.