Deep Reinforcement Learning: Hands-on AI Tutorial in Python
3.9 (132 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16,104 students enrolled

Deep Reinforcement Learning: Hands-on AI Tutorial in Python

Develop Artificial Intelligence Applications using Reinforcement Learning in Python.
3.9 (132 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16,104 students enrolled
Created by Mehdi Mohammadi
Last updated 4/2020
English
English [Auto]
Current price: $104.99 Original price: $149.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 4 hours on-demand video
  • 7 downloadable resources
  • 1 coding exercise
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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.
Course content
Expand all 51 lectures 04:03:57
+ Introduction
3 lectures 04:20
Course Structure
01:23
Environment Setup
01:13
+ Jump into Reinforcement Learning
3 lectures 10:48
RL Applications
03:15
RL vs. Supervised and Unsupervised Learning
04:02
What is reinforcement learning?
2 questions
+ RL Algorithms
9 lectures 38:05
Markov Decision Process
02:38
Optimal Policy
06:47
Bellman Equation
03:20
Q-Learning
02:26
Step-by-step Example
08:08
Sarsa
02:10
Deep Q-Network
05:51
Exploration vs. Exploitation
02:53
Define RL Problem - Examples
03:52
Reinforcement learning algorithms
3 questions
SARSA algorithm
1 question
+ Hands-on Project 1 - Maze Problem
16 lectures 01:21:52
Overall Design
03:05
Create Project
00:53
Create files
01:01
Create Maze Environment class
05:07
Implement Building Maze Grid
13:36
Test build_maze method
01:20
Render and Reset methods
04:19
Implement getting next state and reward
12:38
Create Agent class
04:02
Implement adding states
03:30
Implement choosing action
04:27
Implement learn method
06:25
Create App
02:51
Implement main method
10:01
Implement plotting results
03:52
Run the App
04:45
+ Hands-on Project 2 - Stock Trading
19 lectures 01:46:52
Start project
01:35
Prepare dataset
00:34
Implement getting data
03:05
Implement getting all states
07:15
Implement getting next state and reward
06:56
Create Agent class
08:23
Implement creating deep learning model and reset method
04:54
Implement getting action
10:37
Implement buy and sell
07:42
Implement experience replay
08:26
Create training app
16:06
Test training app
01:01
Create evaluation app
08:14
Implement plotting results
06:30
Run training and evaluation
01:33
Extending Stock Trading with Multiple Features
01:14
Multiple Feature Stock Trader
06:55
Requirements
  • Students are assumed to be familiar with python and have some basic knowledge of statistics, and deep learning.
Description

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.


Who this course is for:
  • Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers.