Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL.

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach.

This course introduces the concepts of Artificial Intelligence and Machine learning. This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure.

Several use cases showcasing different solutions are covered in this video. Introduction to Amazon SageMaker (10 minutes) This course provides an overview of Amazon SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models. Introduction to AWS Greengrass (10 minutes) This course is an introduction to AWS Greengrass, which lets you run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way. You can build machine learning models in the cloud and execute their inference at the edge. Introduction to Amazon Comprehend (10 minutes) This course introduces you to Amazon Comprehend, a new AWS service that helps with natural language processing. In this course, we discuss how Amazon Comprehend solves challenges like the exponential growth of unstructured text, explore the service's five main capabilities, and review some popular use cases.