Machine Learning: Making computers think!

Prepare the iris data by creating train, validation, and test sets before modeling. Train a baseline model, then use SVM and k-means to generate values and visualize clusters with centroids.

Balance bias and variance to improve generalization, distinguishing overfitting from underfitting as models trade training accuracy for test accuracy. Understand how mapping and target functions shape train versus test performance.

Explore methods to improve machine learning accuracy by adding data, engineering features, selecting the best features, combining multiple weak learners through sampling, and tuning models for stronger performance.

Demonstrate the curse of dimensionality by showing how increasing dimensions hinder finding a target, and explain dimensionality reduction as a way to preserve information for classification or regression.