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Reinforcement Learning

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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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Why should you take Reinforcement Learning?

Tractica predicts annual global AI software revenue will grow from $10.1B in 2018 to $126.0B by 2025, achieving a CAGR of 43.41%.

The average salary for a Machine Learning Engineer is $1,18,452 – Glassdoor.co

Machine Learning Engineers rank among the top emerging jobs on LinkedIn

Online self paced classes

Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

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Curriculum

Introduction to Reinforcement Learning

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

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Bandit Algorithms and Markov Decision Process

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

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Dynamic Programming & Temporal Difference Methods

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

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Deep Q Learning

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

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In-class Project

Goal:

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Course Description

About Reinforcement Learning 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

What are the pre-requisites for this Course?

Required Pre-requisites

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

Edureka offers you complimentary self-paced courses

Statistics and Machine learning algorithms
Python Essentials

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Certification

Edureka’s Reinforcement Learning Certificate Holders work at 1000s of companies like

Reinforcement Learning Certificate

John Doe

Reinforcement Learning

with Grade X

XYZ1234 31st Jul 2024

The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate

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Testimonial Reviews

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Video Reviews

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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.

Which project will be part of this Edureka'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.

Features

Self-Paced Learning

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Project based on any of the selected use cases, involving implementation of the various Reinforcement Learning concepts.

Assignments

Each class will be followed by practical assignments.

Lifetime Access

You will get lifetime access to LMS where presentations, quizzes, installation guides & class recordings are available.

Certification

Towards the end of the course, Edureka certifies you as a Reinforcemnt Learning Professional based on the project you submit.

Forum

We have a community forum for our learners that further facilitates learning through peer interaction and knowledge sharing.

FAQ

How soon after Signing up would I get access to the Learning Content?

Post-enrolment, the LMS access will be instantly provided to you and will be available for a lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments.

Is the course material accessible to the students even after the course training is over?

Yes, access to the course material will be available for a lifetime once you have enrolled in the course.

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