Description
Reinforcement Learning Specialization is a training course offered by Coursera that specializes in reinforcement learning.
The reinforcement learning specialized course consists of 4 courses that examine adaptive learning systems and artificial intelligence (AI). Taking full advantage of the potential of artificial intelligence requires the use of reinforcement learning systems. Solutions of Reinforcement learning (RL) can solve real-world problems using trial-and-error interactions and through the full application of reinforcement learning solutions.
By completing this specialized course, you can understand many principles of modern statistics and artificial intelligence. Completing this course also prepares you to take advanced courses and apply artificial intelligence tools to solve real-world problems.
This course is recommended by the University of Alberta and the Alberta Artificial Intelligence Learning Institute, recognized as the world’s leading artificial intelligence center. The rating given to this training 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 taught in this course:
- Creating a reinforcement learning system for sequential decision making
- Familiarity with reinforcement learning algorithms (Temporal-Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, etc.)
- Understanding how to formulate tasks as reinforcement learning problems and how to apply solutions
- Understanding how reinforcement learning can be used in machine learning and how it can complement deep learning and supervised and unsupervised learning.
Course details:
Publisher: Coursera
Level: Advanced
Instructor: Martha White, Adam White
Number of courses: 4 courses
English language
Reinforcement Learning Specialization course headings
- Fundamentals of Reinforcement Learning
- Sample-based Learning Methods
- Prediction and Control with Function Approximation
- A Complete Reinforcement Learning System (Capstone)
Course prerequisite
It is 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)
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File(s) password: www.downloadly.ir
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