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Reinforcement learning is a part of machine learning which covers deep reinforcement learning, art technology, deep learning, and machine learning. In this course, you will learn how to resolve reinforcement learning problems and manage classic examples such as news recommendation, and balance a cart-pole. Further, you will get familiar with basic algorithms, dynamic programming, temporal difference learning, and progress towards deep learning. This course offers an opportunity to learn under the supervision of certified and experienced trainers. Enroll today and learn what it takes to become a certified reinforcement expert.
Who should pursue the Reinforcement Learning certification?
This Artificial Intelligence certification will be ideal for professionals as well as students who have a strong technical background.
Reinforcement learning certification covers the following topics:
- Introduction and Logistics
- Background Review
- Open AI Gym and Basic Reinforcement Learning Techniques
- TD Lambda
- Policy Gradients
- Deep Q-Learning
- Theano and TensorFlow Basics Review
- Appendix
Objectives of the Reinforcement Learning certification
The reinforcement learning certification course consists of some objectives which are given below:
- Deep neural networks of Q-learning
- Learn reinforcement learning with RBF networks
- Understand policy gradient methods with neural networks
- Deep Q-learning with conventional neural networks
- Create various deep learning agents
- Advance reinforcement learning algorithms
Prerequisites
There are some requirements to take Reinforcement Learning certification which are given below:
- Reinforcement learning basics, MDPs, dynamic programming, Monte Carlo, TD learning
- Calculus and probability
- Experience building machine learning models in python and Numpy
- Know how to build a feed forward, convolutional, and recurrent neural network using Theano and sensor flow
What is the duration of the course?
The training and certification in reinforcement will take around 18 hours.
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