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
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
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.