The supervised machine learning algorithms you will learn here are some of the most powerful data science tools you need to solve regression and classification tasks. These are invaluable skills anyone who wants to work as a machine learning engineer and data scientist should have in their toolkit. In this course, you will learn the theory behind all 6 algorithms, and then apply your skills to practical case studies tailored to each one of them, using Python's sci-kit learn library. First, we cover naïve Bayes – a powerful technique based on Bayesian statistics. Its strong point is that it's great at performing tasks in real-time.