Statistical Learning with Python

How can machine learning help you to find interesting patterns in large datasets, generate scientific knowledge, automate complex tasks, and assist organizations in making better decisions?

This course provides an overview of key ideas and m in supervised learning from a statistical perspective. Starting from the essentials of decision theory, model evaluation, and model selection, the course will discuss basic methods such as linear regression and logistic regression, progress to regularized estimation, nonparametric regression, additive models, and decision trees, and conclude with advanced methods such as random forests, gradient boosting, support vector machines, and neural networks.