Comprehensive review of the concepts, methods and models on which machine learning is based. In this module you’ll learn:

Formal notation about ML tasks and definitions Core principles of building an ML algorithms Whole set ML algorithms, from Linear Regression to Random Forest Introduction to core Python packages for ML

We’ll cover the algorithms:

Linear and Logistic Regression kNN and k-Means Decision Trees and Random Forest

We’ll show how to handle classification, regression and clustering tasks.