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Reinforcement Learning

2.1K+ Learners

In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in uncertain environment. 

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Reinforcement Learning Online Course

Jul 25 Sat,Sun (4 Weeks) Weekend Batch Filling Fast 02:30 PM  04:30 PM
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Course Price at

$ 399.00

About Course

In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in uncertain environment.

Who should go for this training?

  • Web Developers
  • Software Developers
  • Programmers
  • Anyone who wants to learn reinforcement learning

Pre-requisites

Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow


Key Skills

reinforcementlearning, Artificial Intelligence, TensorFlow, CNN, Bandit Algorithms, Markov Decision Process, Markov Decision Process, Markov Reward Process, Dynamic Programming, Temporal Difference Methods, Monte Carlo Methods, Deep Q Learning, Policy Gradients

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Course Contents

Download Syllabus

Reinforcement Learning Content

Learning Objectives: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. This module also introduces you to OpenAI Gym - a programming environment used for implementing RL agents.

Topics: 

  • Branches of Machine Learning
  • What is Reinforcement Learning?
  • The Reinforcement Learning Process
  • Elements of Reinforcement Learning
  • RL Agent Taxonomy
  • Reinforcement Learning Problem
  • Introduction to OpenAI Gym
Learning Objectives: The aim of this module is to learn Bandit Algorithms and Markov Decision Process.
Topics: 
  • Bandit Algorithms
  • Markov Process
  • Markov Reward Process
  • Markov Decision Process

Learning Objectives: The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods.
Topics: 
  • Introduction to Dynamic Programming
  • Dynamic Programming Algorithms
  • Monte Carlo Methods
  • Temporal Difference Learning Methods

Learning Objectives: The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning
Topics: 
  • Policy Gradients
  • Policy Gradients using TensorFlow
  • Deep Q learning
  • Q learning with replay buffers, target networks, and CNN

Goal: The aim of this module is to provide you hands-on experience in Reinforcement Learning.

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Projects

What are the system requirements for this Reinforcement Learning Certification Training

The system requirement is a system with an Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system either of 32bit or 64bit.

How will I execute practicals in this Reinforcement Learning Certification Training?

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Which project will be part of this StepLeaf's Reinforcement Learning Online Training Course?

Project Statement: Train an RL Agent to win a Game

Description: 

Using a given Environment in OpenAI Gym, train an RL Agent to accomplish a predefined task. In this project, you will be creating a Neural Network, and applying Policy Gradient Algorithm to train the Agent.

Certification

StepLeaf’s Reinforcement Learning Certificate Holders works at 1000s of MNC Companies All Over the World

FAQ

All the instructors at StepLeaf are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by StepLeaf for providing an awesome learning experience to the participants.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
You will never miss a lecture at StepLeaf! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.  
  • You can attend the missed session, in any other live batch.
Yes, the access to the course material will be available for lifetime once you have enrolled into the course. 

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