Coursera – Reinforcement Learning Specialization 2020-7

Description

Reinforcement Learning Specialization is a training course offered by Coursera that specializes in reinforcement learning.

The Reinforcement Learning course consists of 4 courses that examine adaptive learning systems and artificial intelligence (AI). Making full use of the potential of artificial intelligence requires the use of reinforcement learning systems. Reinforcement Learning Solutions (RL) can solve problems in the real world by using trial and error interactions and by fully utilizing reinforcement learning solutions.

By completing this specialized course, you can understand many of the principles of modern statistics and artificial intelligence. Completing this course will also prepare you for advanced courses and the use of artificial intelligence tools to solve real-world problems.

This course is recommended by the University of Alberta and the Alberta Institute for the Study of Artificial Intelligence, the world's leading artificial intelligence center. The score given to this course by buyers is 4.7 out of 5. By spending 5 hours a week, you can complete this training course in 5 months.

Items that will be taught in this course

Create a reinforcing learning system for sequential decision making

Familiarity with reinforcement learning algorithms (Temporal-Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, etc.)

Understand how to shape tasks as reinforcement learning problems and how to apply solutions

Understanding that reinforcement learning can be used in machine learning and how it can complement deep learning and supervised and unsupervised learning.

Course information

Publisher:: Coursera Level: Advanced Instructor: Martha White, Adam White Number of courses: 4 courses English language

Course Prerequisites

Recommended that learners have at least one year of undergraduate computer science or 2-3 years of professional experience in software development. Experience and comfort with programming in Python required. Must be comfortable converting algorithms and pseudocode into Python. Basic understanding of concepts from statistics (distributions, sampling, expected values), linear algebra (vectors and matrices), and calculus (computing derivatives)

Installation

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p