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Reinforcement Learning Course Certification Training

This course will introduce you to Reinforcement Learning, a subfield of Machine Learning. Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) approaches will be covered. The Value function, Bellman Equation, and Value iteration will be introduced to you. You will also be introduced to Policy Gradient techniques. You will get experience making judgements in an unpredictable setting.

Why This Course

Tractica forecasts that yearly worldwide AI software sales would increase from $10.1 billion in 2018 to $126.0 billion by 2025, at a CAGR of 43.41%.

A Machine Learning Engineer's annual pay is $1,18,452. - www.glassdoor.co

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Instructor-led live online classes

Reinforcement Learning

Self paced classes

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Why Enroll In Reinforcement Course?

Reinforcement learning is a type of machine learning that trains agents to make decisions and take actions to maximize rewards in real-world problems. Its benefits include versatility, adaptability, and the ability to find optimal solutions in complex environments, making it a powerful tool for improving performance and reducing costs in various industries.

Reinforcement Training Features

Live Interactive Learning

  World-Class Instructors

  Expert-Led Mentoring Sessions

  Instant doubt clearing

Lifetime Access

  Course Access Never Expires

  Free Access to Future Updates

  Unlimited Access to Course Content

24x7 Support

  One-On-One Learning Assistance

  Help Desk Support

  Resolve Doubts in Real-time

Hands-On Project Based Learning

  Industry-Relevant Projects

  Course Demo Dataset & Files

  Quizzes & Assignments

Industry Recognized Certification

  CertHippo Training Certificate

  Graded Performance Certificate

  Certificate of Completion

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Designing Identity Solutions

Reinforcement Course Curriculum

Learning Objectives: This module's goal is to teach you the principles of Reinforcement Learning and its components. This lecture will also introduce you to OpenAI Gym, a programming environment for developing 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 goal of this subject is to teach you about Bandit Algorithms and the Markov Decision Process.

Topics:

  • Bandit Algorithms

  • Markov Process

  • Markov Reward Process

  • Markov Decision Process

Learning Objectives: The goal of this session is to teach you about Dynamic Programming Algorithms and Temporal Difference Learning techniques.

Topics:

  • Introduction to Dynamic Programming

  • Dynamic Programming Algorithms

  • Monte Carlo Methods

  • Temporal Difference Learning Methods

Learning Objectives:  The goal of this session is to teach you about Policy Gradients and Deep Q Learning.


Topics:

  • Policy Gradients

  • Policy Gradients using TensorFlow

  • Deep Q learning

  • Q learning with replay buffers, target networks, and CNN

Goal: This module's goal is to provide you hands-on experience with Reinforcement Learning.

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Reinforcement Online Training FAQs

Following enrollment, you will have immediate access to the LMS and will have it for the rest of your life. You will get access to all past class recordings, PPTs, PDFs, and assignments. 

Yes, once you join in the course, you will have lifetime access to the course material.

Reinforcement Course Description

About Reinforcement Learning Course

This course will introduce you to Reinforcement Learning, a subfield of Machine Learning. Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) approaches will be covered. The Value function, Bellman Equation, and Value iteration will be introduced to you. You will also be introduced to Policy Gradient techniques. You will get experience making judgements in an unpredictable setting.

Who should go for this training?

  • Web Developers

  • Software Developers

  • Programmers

  • Anyone who wants to learn reinforcement learning

What are the prerequisites for this Course?

Required Prerequisites

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

CertHippo offers you complimentary self-paced courses

  • Statistics and Machine learning algorithms

  • Python Essentials

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Projects

A PC with an Intel i3 CPU or above, at least 3GB RAM (4GB preferred), and a 32bit or 64bit operating system are required.

Project Statement: Train an RL Agent to win a Game

Description: Train an RL Agent in OpenAI Gym using a provided Environment to complete a preset goal. In this project, you will create a Neural Network and train the Agent using the Policy Gradient Algorithm.

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