Reinforcement Learning with Python Course

An in-depth grasp of reinforcement learning and its real-world applications can be gained in a week by enrolling in the Oxford Training Center’s Reinforcement Learning with Python course. Using Python, participants will become proficient in fundamental ideas, sophisticated methods, and practical application. Real-world use examples from various industries, deep reinforcement learning, and applied reinforcement learning algorithms are among the subjects covered in the course. With an emphasis on useful skills, participants will discover how to use cutting-edge tools and techniques to create, execute, and optimize AI-driven solutions.

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Objectives and target group

Objectives

The Reinforcement Learning with Python course is structured to achieve the following objectives:

  • Establish an extremely solid understanding of the key ideas behind reinforcement learning and how to apply those in real-life applications.
  • Mastering Algorithms: Understand how to run and optimize reinforcement learning algorithms by using Python for Q-learning, policy gradient methods, deep Q-network-DQN.
  • Applied Skills: Understand the usage of Python to apply deep reinforcement learning in robotics, game AI, and financial modeling.
  • Hands-on Experience: Engage in interactive sessions, coding exercises, and projects that cover hands-on reinforcement learning with Python, keeping in mind practical proficiency.
  • Advanced Topics: Get an understanding of how state-of-the-art applied reinforcement learning is done using Python, including integrations with cloud computing platforms like AWS.

Target Group

This course is tailored for professionals and enthusiasts eager to master reinforcement learning. The ideal participants include:

  • Data Scientists and Machine Learning Engineers: Expand your expertise by integrating practical AI with Python and reinforcement learning into your workflow.
  • Software Developers: Learn to implement AI-driven features in applications using reinforcement learning algorithms with Python.
  • Researchers and Academics: Delve into advanced deep reinforcement learning with Python techniques for groundbreaking research.
  • AI Enthusiasts and Hobbyists: Gain hands-on experience with applied reinforcement learning with Python to kickstart your journey in AI.
  • Business Analysts and Managers: Understand the potential of reinforcement learning with Python to drive data-driven decision-making in various industries.

Course Content

The Reinforcement Learning with Python course offers an in-depth and structured curriculum:

  1. Introduction to Reinforcement Learning
  • An overview of the ideas and uses of reinforcement learning
  • The distinction between reinforcement learning, supervised learning, and unsupervised learning
  1. Fundamentals of Reinforcement Learning
  • Reinforcement Learning Foundations
    MDPs, or Markov decision processes
    Policies and functions for rewards
    Trade-offs between exploration and exploitation
  1. Reinforcement Learning Algorithms
  • Actor-critic models and policy-based approaches; Q-learning and SARSA algorithms
  • Deep reinforcement learning with Python: An overview of neural networks in reinforcement learning
  1. Advanced Topics and Applications
  • Practical AI with Python and reinforcement learning: Implementing RL for robotics, gaming, and autonomous systems
  • Cloud-based reinforcement learning: Integrating with AWS and other platforms
  • Scaling RL models for real-world applications
  1. Hands-On Projects and Case Studies
  • Designing an AI agent for a game simulation
  • Building and optimizing a reinforcement learning model for stock trading
  • Implementing Amazon’s hands-on reinforcement learning with Python using industry-relevant datasets
  1. Final Assessment and Certification
  • Capstone project presentation
  • Certification of completion from Oxford Training Centre

 

Course Dates

January 20, 2025
February 10, 2025
March 24, 2025
April 21, 2025

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