CertAchiever

Reinforcement Learning

contact us: +918882136965

Instructor-led live online classes

Weekday Classes | Weekend Classes |

Reinforcement Learning

$259

Reinforcement Learning

In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods.

Training Features

Instructor-led Sessions

Both Weekdays and Weekend batches are available.

Real-Life Case Studies

During the sessions you will be working on the case studies provided by the trainer.

Lifetime Access

You will get the life time access of training material including videos & study guides..

Assignments

Each class will be followed by practical assignments to practice the concepts covered in class

24 x 7 Expert Support

We have a lifetime 24x7 online support team to resolve all your technical queries,through a ticket based tracking system..

Certification

Towards the end of the course, you will be awarded with Training competition certification based on you performance.

Course Curriculum

Learning Objectives

The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. This module also introduces you to OpenAI Gym - a programming environment used for implementing RL agents.

Topics Covered

Branches of Machine Learning, What is Reinforcement Learning?, The Reinforcement Learning Process, Elements of Reinforcement Learning, RL Agent Taxonomy, Reinforcement Learning Problem, Introduction to OpenAI Gym

Learning Objectives

The aim of this module is to learn Bandit Algorithms and Markov Decision Process.

Topics Covered

Bandit Algorithms, Markov Process, Markov Reward Process, Markov Decision Process

Learning Objectives

The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods.

Topics Covered

Introduction to Dynamic Programming, Dynamic Programming Algorithms, Monte Carlo Methods, Temporal Difference Learning Methods

Learning Objectives

The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning

Topics Covered

Policy Gradients, Policy Gradients using TensorFlow, Deep Q learning, Q learning with replay buffers, target networks, and CNN

Learning Objectives

The aim of this module is to provide you hands-on experience in Reinforcement Learning.

Topics Covered

FAQ's

You will never miss a lecture at CertAchiever! You can choose either of the two options:

View the recorded session of the class available in your LMS.

·You can attend the missed session, in any other live batch."

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

You can give us a CALL at +918882136965 (IN) OR email at admin@certachiever.com

Certification

Once your training will complete, you will be awarded with training completion certification from the Trainer.