Deep Reinforcement Learning- Hands-on AI Tutorial in Python

Deep Reinforcement Learning: Hands-on AI Tutorial in Python, available at $59.99, has an average rating of 3.9, with 51 lectures, 3 quizzes, based on 219 reviews, and has 17424 subscribers.

You will learn about The concepts and fundamentals of reinforcement learning The main algorithms including Q-Learning, SARSA as well as Deep Q-Learning. How to formulate a problem in the context of reinforcement learning and MDP. Apply the learned techniques to some hands-on experiments and real world projects. Develop artificial intelligence applications using reinforcement learning. This course is ideal for individuals who are Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers. It is particularly useful for Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers.

Enroll now: Deep Reinforcement Learning: Hands-on AI Tutorial in Python

Summary

Title: Deep Reinforcement Learning: Hands-on AI Tutorial in Python

Price: $59.99

Average Rating: 3.9

Number of Lectures: 51

Number of Quizzes: 3

Number of Published Lectures: 51

Number of Published Quizzes: 3

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

The concepts and fundamentals of reinforcement learning

The main algorithms including Q-Learning, SARSA as well as Deep Q-Learning.

How to formulate a problem in the context of reinforcement learning and MDP.

Apply the learned techniques to some hands-on experiments and real world projects.

Develop artificial intelligence applications using reinforcement learning.

Who Should Attend

Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers.

Target Audiences

Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers.

In this course we learn the concepts and fundamentals of reinforcement learning, it’s relation to artificial intelligence and machine learning, and how we can formulate a problem in the context of reinforcement learning and Markov Decision Process. We cover different fundamental algorithms including Q-Learning, SARSA as well as Deep Q-Learning. We present the whole implementation of two projects from scratch with Q-learning and Deep Q-Network.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Structure

Lecture 3: Environment Setup

Chapter 2: Jump into Reinforcement Learning

Lecture 1: Introduction

Lecture 2: RL Applications

Lecture 3: RL vs. Supervised and Unsupervised Learning

Chapter 3: RL Algorithms

Lecture 1: Markov Decision Process

Lecture 2: Optimal Policy

Lecture 3: Bellman Equation

Lecture 4: Q-Learning

Lecture 5: Step-by-step Example

Lecture 6: Sarsa

Lecture 7: Deep Q-Network

Lecture 8: Exploration vs. Exploitation

Lecture 9: Define RL Problem – Examples

Chapter 4: Hands-on Project 1 – Maze Problem

Lecture 1: Overall Design

Lecture 2: Create Project

Lecture 3: Create files

Lecture 4: Create Maze Environment class

Lecture 5: Implement Building Maze Grid

Lecture 6: Test build_maze method

Lecture 7: Render and Reset methods

Lecture 8: Implement getting next state and reward

Lecture 9: Create Agent class

Lecture 10: Implement adding states

Lecture 11: Implement choosing action

Lecture 12: Implement learn method

Lecture 13: Create App

Lecture 14: Implement main method

Lecture 15: Implement plotting results

Lecture 16: Run the App

Chapter 5: Hands-on Project 2 – Stock Trading

Lecture 1: Overall Design

Lecture 2: Start project

Lecture 3: Prepare dataset

Lecture 4: Create Market Environment class

Lecture 5: Implement getting data

Lecture 6: Implement getting all states

Lecture 7: Implement getting next state and reward

Lecture 8: Create Agent class

Lecture 9: Implement creating deep learning model and reset method

Lecture 10: Implement getting action

Lecture 11: Implement buy and sell

Lecture 12: Implement experience replay

Lecture 13: Create training app

Lecture 14: Test training app

Lecture 15: Create evaluation app

Lecture 16: Implement plotting results

Lecture 17: Run training and evaluation

Lecture 18: Extending Stock Trading with Multiple Features

Lecture 19: Multiple Feature Stock Trader

Chapter 6: Summary

Lecture 1: Summary

Instructors

Mehdi Mohammadi
Machine Learning Engineer

Rating Distribution

1 stars: 10 votes

2 stars: 12 votes

3 stars: 44 votes

4 stars: 82 votes

5 stars: 71 votes

Frequently Asked Questions

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