Descriptions

This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.

This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.

This course is divided into three parts and covers the following topics:

Part 1 (Tabular methods):

• Markov decision process
• Dynamic programming
• Monte Carlo methods
• Time difference methods (SARSA, Q-Learning)
• N-step bootstrapping

Part 2 (Continuous state spaces):

• State aggregation
• Tile Coding

Part 3 (Deep Reinforcement Learning):

• Deep SARSA
• Deep Q-Learning
• REINFORCE
• Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method)

What you’ll learn

• Understand the Reinforcement Learning paradigm and the tasks that it’s best suited to solve.
• Understand the process of solving a cognitive task using Reinforcement Learning
• Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting
• Implement Reinforcement Learning algorithms completely from scratch
• Fundamentally understand the learning process for each algorithm
• Debug and extend the algorithms presented
• Understand and implement new algorithms from research papers

Who this course is for

• Developers who want to get a job in Machine Learning
• Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
• Researchers/scholars seeking to enhance their practical coding skills.

Specificatoin of Reinforcement Learning beginner to master – AI in Python

• Publisher : Udemy
• Teacher : Escape Velocity Labs
• Language: English
• Level : All Levels
• Lectures : 134
• Duration : 10 hours and 47 minutes

Content

Requirements

• Be comfortable programming in Python
• Know basic linear algebra and calculus (matrices, vectors, determinants, derivatives, etc.)
• Know basic statistics and probability theory (mean, variance, normal distribution, etc.)

Pictures of Reinforcement Learning beginner to master – AI in Python

Sample Video

Installation Guide

Extract the files and watch with your favorite player

Subtitle: English

Quality: 1080p

Version 2022/10 compared to 2022/6 has decreased by 16 minute and increased by 9 lessons

The 2024/11 version has increased the number of lessons by 2 compared to 2020/11. The course quality has also increased from 720p to 1080p.

The 2025/5 version has increased the number of lessons by 1 and the duration increased by 1 minutes compared to 2024/11.

Download Link

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 645 MB

Password file(s): www.downloadly.ir

File size

2.63 GB