Reinforcement Learning beginner to master - AI in Python

Course Feature

Cost

Paid
Provider

Udemy
Certificate

Paid Certification
Language

English
Start Date

2022-12-28
Learners

No Information
Duration

No Information
Instructor

Escape Velocity Labs

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4.3

4,976 Ratings

This Reinforcement Learning course on Udemy is the most comprehensive one available. It covers the three paradigms of modern artificial intelligence, and teaches you how to implement adaptive algorithms from scratch. You will 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 understand new algorithms as they emerge, and prepare you for more advanced courses. It is focused on developing practical skills, and you will implement algorithms in jupyter notebooks from scratch. Don't miss this opportunity to master Reinforcement Learning and AI in Python!

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Reinforcement Learning beginner to master - AI in Python

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Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 18th, 2023]

Skills and Knowledge:
By taking this course, learners will acquire a comprehensive understanding of Reinforcement Learning, including the basics of Markov decision process, dynamic programming, Monte Carlo methods, time difference methods, N-step bootstrapping, state aggregation, tile coding, deep SARSA, deep Q-Learning, REINFORCE, and Advantage Actor-Critic / A2C. Learners will also gain practical skills in implementing these algorithms from scratch in jupyter notebooks.
Professional Growth:
This course contributes to professional growth by providing a comprehensive overview of the three paradigms of modern artificial intelligence, specifically Reinforcement Learning. It covers the basics of Reinforcement Learning, as well as more advanced algorithms, and provides practical skills by implementing algorithms from scratch in jupyter notebooks. This course will give learners the foundation they need to understand new algorithms as they emerge, and prepare them for more advanced courses in the series.
Further Education:
This course is suitable for preparing further education in the field of Reinforcement Learning. It covers the basics of Reinforcement Learning, as well as more advanced algorithms, and provides practical skills in implementing algorithms from scratch. It also prepares learners for more advanced courses in the series, which will go deeper into different branches of Reinforcement Learning.

Course Syllabus

Welcome module

The Markov decision process (MDP)

Dynamic Programming

Monte Carlo methods

Temporal difference methods

N-step bootstrapping

Continuous state spaces

Brief introduction to neural networks

Deep SARSA

Deep Q-Learning

REINFORCE

Advantage Actor-Critic (A2C)

Outro

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