MLU-Explain is a set of visual machine learning explainers from Amazon Machine Learning University. It covers foundational topics such as decision trees, random forests, linear regression, logistic regression, neural networks, cross validation, bias, and variance.
Instead of relying only on formulas, each article uses visualizations of data points, decision boundaries, model outputs, and evaluation metrics. It is a useful series for beginners who want an intuitive grasp of what happens when models are selected, trained, and evaluated.
