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Markov Decision Processes (MDPs) – CR000149

Original price was: ₹4,500.00.Current price is: ₹800.00.



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Subject – Reinforcement Learning and Autonomous Agents

Industry – Machine Learning and AI

Introduction:

Welcome to the eLearning course on Markov Decision Processes (MDPs) offered by T24Global Company. In this course, we will explore the fundamental concepts of MDPs and their applications in the field of Machine Learning and Artificial Intelligence (AI).

Machine Learning and AI have revolutionized various industries by enabling computers to learn from data and make intelligent decisions. MDPs serve as a powerful framework for modeling decision-making problems in dynamic environments, making them an essential tool for AI practitioners.

So, what exactly are Markov Decision Processes (MDPs)? MDPs are mathematical models used to formalize decision-making problems in situations where outcomes are uncertain. They provide a way to represent the interaction between an agent and its environment over time, allowing the agent to make informed decisions based on the current state and future rewards.

MDPs are widely used in various real-world applications, such as robotics, finance, healthcare, and game theory. For example, in robotics, MDPs can be used to model the movement of a robot in an environment and determine the optimal actions to achieve a specific goal. In finance, MDPs can be employed to optimize investment strategies by considering the uncertain nature of the market.

In this course, we will cover the key components of MDPs, including states, actions, transition probabilities, and rewards. We will delve into the mathematical foundations of MDPs and discuss how they can be used to model and solve decision-making problems.

Furthermore, we will explore various solution methods for MDPs, such as value iteration, policy iteration, and Q-learning. These algorithms provide efficient ways to find optimal policies that maximize the expected cumulative rewards over time.

Throughout the course, we will provide practical examples and hands-on exercises to reinforce your understanding of MDPs and their applications. By the end of the course, you will have a solid foundation in MDPs and be equipped with the knowledge to apply them in real-world scenarios.

Whether you are a student, a professional, or an AI enthusiast, this course will provide you with the necessary skills to tackle decision-making problems using MDPs. Our experienced instructors will guide you through the course material, ensuring a comprehensive and engaging learning experience.

So, join us on this exciting journey into the world of Markov Decision Processes and discover how they can enhance your understanding of Machine Learning and AI. Let’s get started!

NOTE – Post purchase, you can access your course at this URL – https://mnethhil.elementor.cloud/courses/markov-decision-processes-mdps/ (copy URL)

 

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Lessons Included

 

LS004333 – Ethical Considerations in RL and Autonomous Agents

LS003287 – Telecom Policy Development and Implementation

LS003287 – Telecom Policy Development and Implementation

LS002241 – Policy Gradient Methods

LS001195 – Q-Learning and Deep Q-Networks (DQNs)

LS000149 – Policy and Value Iteration

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