In this era of big data, there is an increasing need to develop and deploy algorithms that can analyze and identify connections in that data. Using machine learning (a subset of artificial intelligence) it is now possible to create computer systems that automatically improve with experience. This technology has numerous real-world applications including robotic control, data mining, autonomous navigation, and bioinformatics. This course features classroom videos and assignments adapted from the CS229 graduate course as delivered on-campus at Stanford in Autumn 2018 and Autumn 2019. In order to make the content and workload more manageable for working professionals, the course has been split into two parts, XCS229i: Machine Learning and XCS229ii: Machine Learning Strategy and Intro to Reinforcement Learning.

NOTE: This course is a continuation of XCS229i: Machine Learning. Though not strictly required, it is highly recommended to take XCS229i before enrolling in XCS229ii, as assignments assume knowledge of topics in the first course. As machine learning models grow in sophistication, it is increasingly important for its practitioners to be comfortable navigating their many tuning parameters. Through video lectures and hands-on exercises, this course will equip you with the knowledge to get the most out of your data. You will learn the concepts and techniques you need to guide teams of ML practitioners.