- 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. You will learn to make decisions in uncertain environment.
Reinforcement Learning
Instructor
Mike
- Description
- Curriculum
- FAQ
- Reviews
- Web Developers
- Software Developers
- Programmers
- Anyone who wants to learn reinforcement learning
Required Pre-requisites
- Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow
Edureka offers you complimentary self-paced courses
- Statistics and Machine learning algorithms
- Python Essentials
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1
Introduction to Reinforcement LearningLearning 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:
- 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
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2
Bandit Algorithms and Markov Decision Process
Learning Objectives: The aim of this module is to learn Bandit Algorithms and Markov Decision Process.
Topics:
- Bandit Algorithms
- Markov Process
- Markov Reward Process
- Markov Decision Process
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3
Dynamic Programming
Learning Objectives: The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods.
Topics:
- Introduction to Dynamic Programming
- Dynamic Programming Algorithms
- Monte Carlo Methods
- Temporal Difference Learning Methods
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4
Deep Q Learning
Learning Objectives: The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning
Topics:
- Policy Gradients
- Policy Gradients using TensorFlow
- Deep Q learning
- Q learning with replay buffers, target networks, and CNN
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5
In-Class Project
Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session.
Objectives: At the end of this module, you should be able to:
- How to approach a project
- Hands-On project implementation
- What Industry expects
- Industry insights for the Machine Learning domain
- QA and Doubt Clearing Session
What if I miss a class?
"You will never miss a lecture at Certs Learning! 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."
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch."
What if I have queries after I complete this course?
Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.
How soon after Signing up would I get access to the Learning Content?
Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24x7 support team will be granted instantly as well. You can start learning right away.
Is the course material accessible to the students even after the course training is over?
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.
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