This course is part of the Deep Learning and Python Programming for AI with Microsoft Azure ExpertTrack
Deep Learning on Azure with Python: Reinforcement Learning
Discover reinforcement learning in this course covering how to frame reinforcement learning problems, algorithms, and more.
Duration
6 weeksWeekly study
5 hours100% online
How it worksIncluded in an ExpertTrack
Course 5 of 5
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.
What topics will you cover?
- Bandits
- Approximate Solution Methods
- Policy Gradient
- Actor-Critic RL that Works.
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Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Debated Reinforcement Learning Problem
- Applied Markov Decision Process
- Applied Dynamic Programming
- Applied Temporal Difference Learning
Who is the course for?
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.
Who developed the course?
CloudSwyft Global Systems, Inc.
CloudSwyft has partnered with the top global technology companies to deliver cutting edge digital skills learning across the modern workplace.
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- Pay $39 per month to keep learning online
- Have complete control over your subscription; you can cancel any time
- Work at your own pace and set your own deadlines at every stage
- Only pay while you’re learning; the subscription will cancel automatically when you finish
- Complete online assessments to test your knowledge and prove your skills
- Earn digital course certificates and a final award that you can share online, with potential employers, and your professional network
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Learning on FutureLearn
Your learning, your rules
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- Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
- Stay motivated by using the Progress page to keep track of your step completion and assessment scores
Join a global classroom
- Experience the power of social learning, and get inspired by an international network of learners
- Share ideas with your peers and course educators on every step of the course
- Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others
Map your progress
- As you work through the course, use notifications and the Progress page to guide your learning
- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
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