Online or onsite, instructor-led live Reinforcement Learning training courses demonstrate through interactive hands-on practice how to create and deploy a Reinforcement Learning system.
Reinforcement Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Reinforcement Learning training can be carried out locally on customer premises in the UAE or in NobleProg corporate training centers in the UAE.
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Testimonials
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Amr Alaa - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
learning new language.
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
helpful and good listener .. interactive
Ahmed El Kholy - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course
Mohamed Ghowaiba - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
the course is very interesting being the main focus nowdays
mohamed taher - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
The discussions to broaden our horizons
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
I like examples to explain
AUO友达光电（苏州）有限公司
Course: OptaPlanner in Practice
Machine Translated
Reinforcement Learning Course Outlines in the UAE
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
Overview
This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.
By the end of this training, participants will be able to:
- Install and apply the libraries and programming language needed to implement Reinforcement Learning.
- Create a software agent that is capable of learning through feedback instead of through supervised learning.
- Program an agent to solve problems where decision making is sequential and finite.
- Apply knowledge to design software that can learn in a way similar to how humans learn.
By the end of this training, participants will be able to:
- Install and apply the libraries and programming language needed to implement Reinforcement Learning.
- Create a software agent that is capable of learning through feedback instead of through supervised learning.
- Program an agent to solve problems where decision making is sequential and finite.
- Apply knowledge to design software that can learn in a way similar to how humans learn.
21 hours
Overview
This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
By the end of this training, participants will be able to:
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
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