Unsupervised Learning: Clustering and PCA

In this lesson, we will learn Unsupervised Learning, Clustering, and PCA. We will explain finding patterns without a target, customer segmentation, K-Means, cluster interpretation, dimensionality reduction, and PCA logic. ## Lesson objective In this lesson, we will learn Unsupervised Learning: Clustering and PCA. In the previous lesson, we covered Overfitting, Cross-Validation and Model Tuning. We explained overfitting, underfitting, good fit, train/test performance, generalization, bias-variance trade-off, hyperparameters, validation data, cross-validation, grid search, random search, early stopping, and regularization.