Reinforcement Learning Course Certification Training

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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

Machine Learning Engineers rank among the top emerging jobs on LinkedIn

Why Enroll In 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.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Introduction to Reinforcement Learning

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

Bandit Algorithms and Markov Decision Process

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

Dynamic Programming & Temporal Difference Methods

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

Deep Q Learning

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

In-class Project

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

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Online Training FAQs

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

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.

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

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

Course Description

About Reinforcement Learning Course

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

EduCerts offers you complimentary self-paced courses

Statistics and Machine learning algorithms
Python Essentials

Projects

What system requirements are there for this Reinforcement Learning Certification Training?

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

Which project will be part of this EduCerts Reinforcement Learning Online Training Course?

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.