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Practical Reinforcement Learning

Pavel Shvechikov and Alexander Panin
Welcome to the Reinforcement Learning online course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also...
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Welcome to the Reinforcement Learning online course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun! Do you have technical problems? Write to us: [email protected].
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines reinforcement learning algorithms, which is the foundation for sequential decision making tasks
Taught by Alexander Panin and Pavel Shvechikov, who are recognized for their work in reinforcement learning research and education
Develops the skills for developing and implementing reinforcement learning algorithms
Emphasizes practical applications of reinforcement learning, using concepts such as contextual bandits and seq2seq

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Reviews summary

Practical reinforcement learning

This well-structured course uses a hands-on approach to teach the basics of Reinforcement Learning. It features practical assignments in Jupyter notebooks and a solid mix of theory in videos with links to additional materials. Despite some accent challenges, the lecturers, especially Alexander Panin, bring enthusiasm and expertise to the subject. The course is a great choice for those with a strong foundation in probability, statistics, and programming, but be prepared for some challenging assignments.
Knowledgeable instructors, especially Alexander Panin
"Loved the course, both the lecturers, especially Alexander Panin's enthusiasm kept me going and motivated for the course."
Assignments test understanding but may require extra effort
"Great course and it has challenging assignments"
Hands-on experience through practical assignments
"Practical assignments are well calibrated - the bare minimum to pass is quite easy to achieve, and more material is available for the more dedicated students."
Insufficient theoretical explanations
"The theoretical material regarding the covered methods and algorithms was generally lacking."
Difficulty understanding instructors' accents
"Instructors are difficult to understand."
"Accent is tough to understand sometimes."

Career center

Learners who complete Practical Reinforcement Learning will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use machine learning and statistical modeling to extract insights from data. This course can help Data Scientists build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Data Scientists can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course can help Machine Learning Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Machine Learning Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Computer Scientist
Computer Scientists conduct research on computer science and develop new technologies. This course can help Computer Scientists build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Computer Scientists can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help Software Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Software Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Mechanical Engineer
Mechanical Engineers design, develop, and test mechanical systems. This course can help Mechanical Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize manufacturing processes, control robots, and develop self-driving cars. By taking this course, Mechanical Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Electrical Engineer
Electrical Engineers design, develop, and test electrical systems. This course can help Electrical Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize power systems, control robots, and develop self-driving cars. By taking this course, Electrical Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help Quantitative Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop trading strategies, optimize portfolios, and manage risk. By taking this course, Quantitative Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex decision-making problems. This course can help Operations Research Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize supply chains, manage inventory, and schedule resources. By taking this course, Operations Research Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Statistician
Statisticians collect, analyze, and interpret data. This course can help Statisticians build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop predictive models, identify trends, and optimize decision-making. By taking this course, Statisticians can learn the latest techniques in reinforcement learning and apply them to real-world problems.
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. This course may help Business Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize business processes, improve customer satisfaction, and reduce costs. By taking this course, Business Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.
Product Manager
Product Managers develop and manage products. This course may help Product Managers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize product features, pricing, and marketing campaigns. By taking this course, Product Managers can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.
Financial Analyst
Financial Analysts analyze financial data and make investment recommendations. This course may help Financial Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop trading strategies, optimize portfolios, and manage risk. By taking this course, Financial Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course focuses primarily on deep neural networks and game playing.
Consultant
Consultants provide advice and guidance to businesses and organizations. This course may help Consultants build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize business processes, improve customer satisfaction, and reduce costs. By taking this course, Consultants can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.
Data Analyst
Data Analysts collect, analyze, and interpret data. This course may help Data Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop predictive models, identify trends, and optimize decision-making. By taking this course, Data Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course places stronger emphasis on deep neural networks and game playing.
Market Researcher
Market Researchers conduct research on consumer behavior and market trends. This course may help Market Researchers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop market segmentation models, predict consumer behavior, and optimize marketing campaigns. By taking this course, Market Researchers can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course focuses primarily on deep neural networks and game playing.

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