Deep Learning on Azure with Python: Reinforcement Learning

This machine learning course focuses on reinforcement learning and how it uses artificial intelligence to find the best possible solution to complex problems involving multiple decisions.

Use reinforcement learning for complex problem solving

Reinforcement learning acknowledges the multifaceted, multilevel nature of the problems we use machine learning to solve. These challenges might be viewed as a sequence, with each resolved challenge creating or limiting possibilities to solve the next. Framing these challenges as relational learning problems allows us to explore every potential path through a sequence of decisions. This allows artificial intelligence to determine the most effective or efficient solution to complex problems.

Reinforcement learning can be applied to neural networks used in deep learning, helping us to build more refined algorithms.

Explore dynamic programming algorithms and more

This course will give you an introduction to reinforcement learning using Python, in Microsoft Azure. You’ll learn how to frame relational learning problems. You’ll get an introduction to common relational learning algorithms, including dynamic programming algorithms and temporal difference learning. And you’ll discover Project Malmo – a platform for AI experimentation built in Minecraft.

Frame reinforcement learning problems in Azure with Python

By the end of this course, you will have developed a clear understanding of reinforcement learning techniques, and the relevant formal notation. You’ll then be able to apply these in Microsoft Azure Cognitive Services, using Python programming.

This machine learning and artificial intelligence course is designed for those who would like to learn more about deep learning. Basic knowledge of python programming would be advantageous, as would solid maths and computer science skills.

This machine learning and artificial intelligence course is designed for those who would like to learn more about deep learning. Basic knowledge of python programming would be advantageous, as would solid maths and computer science skills.

• Debated Reinforcement Learning Problem
• Applied Markov Decision Process
• Applied Dynamic Programming
• Applied Temporal Difference Learning