Coursera – Reinforcement Learning Specialization 2020-7

Description

Reinforcement Learning, Specialization, etc. Course offered by Coursera that covers the special topic of Reinforcement Learning.

The Learning Reinforcement course consists of 4 courses reviewing adaptive learning and artificial intelligence (AI) systems. To fully exploit the potential of artificial intelligence, the use of a reinforcement learning system is required. Solutions for Reinforcement learning (RL) can utilize the interactions of trial and error and apply the complete solutions of reinforcement learning to solve the problems in the real world.

Completing this specialized course will enable you to understand many of the principles of modern statistics and artificial intelligence. After completing this course, you will also need to pass the advanced courses and apply the tools of artificial intelligence to solve real-world problems.

This course is designated as the center of the most important artificial intelligence in the world by the University of Alberta and the Institute of Learning, Artificial Intelligence, Alberta. recommended. Buyers rate the course 4.7 out of 5. If you dedicate 5 hours a week, you can complete this course in 5 months, etc. complete.

Cases in which the course is taught:

Create a reinforcement learning system to make decisions sequentially
Familiarity with reinforcement learning algorithms (Temporal – Difference Learning, Monte Carlo, Sarsa, Q-Learning, Policy Gradients, Dyna, etc.)
Understand how tooling responsibilities become reinforcement learning topics and how solutions are applied
Understand that reinforcement learning can be used in machine learning and how deep learning and learning supervision and monitoring can be unhelpful.

Profile courses:

Publisher: : Coursera
Level: Advanced
Lecturer: Martha White, Adam White
Number of courses: 4 courses
Language: English

This course serves to specialize in reinforcement learning

Basics of Reinforcement Learning
Example-based learning methods
Prediction and control with function approximation
A complete reinforcement learning system (Capstone)

Prerequisite course

It is recommended that learners have at least one year of undergraduate computer science studies or two to three years of professional experience in software development. Experience and knowledge of programming in Python required. Must be familiar with converting algorithms and pseudocode to Python. Basic understanding of concepts from statistics (distributions, samples, expected values), linear algebra (vectors and matrices), and calculus (calculating derivatives)