Advanced Machine Learning with scikit-learn

Andreas Mueller, an expert in machine learning with scikit-learn, will show you how to select and evaluate machine learning models. This course is for Python users who already have some experience.
Starting with model complexity, overfitting, and underfitting. Andreas will then teach you about pipelines and advanced metrics, imbalanced classes, as well as model selection for unsupervised Learning. This tutorial also covers how to deal with incomplete data, categorical variables, and dictionaries. Finally, you'll learn about out-of-core learning, including the scilearn interface to out-of-core learning and kernel approximations in large-scale nonlinear classification.

This computer-based training course will teach you everything you need to know in order to select and evaluate machine learning models. You can follow the author through the lessons by downloading the working files.