Explore the intricacies of SHAP (SHapley Additive exPlanations) and Shapley Values in this 21-minute video tutorial on Machine Learning Explainability. Dive into a comprehensive recap of SHAP, followed by an in-depth examination of SHAP Summary Plots and SHAP Dependence Plots. Learn how to interpret these powerful tools for understanding model predictions and feature importance. Gain valuable insights into making machine learning models more interpretable and explainable, enhancing your data science and ML skills. This tutorial is part of the Kaggle 30 Days of ML Challenge, designed to help you build a daily habit of coding machine learning in Python.