Reinforcement Learning Online Course | Edureka
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Reinforcement Learning

Why should you take Reinforcement Learning course ?

  • It is predicted that about $47 billion will be budgeted towards machine learning in 2020 – Analyticsinsight.net
  • The average salary for a Machine Learning Engineer is $1,18,452 – Glassdoor.co
  • Machine Learning Engineers rank among the top emerging jobs on LinkedIn
  • 2K + satisfied learners. Reviews
About the Course

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.

Instructor-led Reinforcement Learning live online classes

02 nd  Feb
Sat & Sun (2.5 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)

Course Price

254 299
15% OFF
    Expires in
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EMI starts at 3186 / month.

    Goal: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. To learn Decision Making, Monte Carlo Approach and Temporal Difference Learning.

Topics:
  • Branches of Machine Learning
  • What is Reinforcement Learning?
  • Reinforcement Learning - How does it differ from other machine learning paradigms
  • Comparing RL with other ML techniques
  • Elements of Reinforcement Learning
  • The Reinforcement Learning Process
  • Rewards
  • Agent and Environment
  • Types of Tasks (Episodic and Continuous Tasks)
  • Ways of Learning (Monte Carlo Approach and Temporal Difference Learning)
  • Exploration and Exploitation Trade off
  • Approaches to Decision Making in RL
  • Most used Reinforcement Learning algorithm (Q-learning)
  • Practical applications of Reinforcement Learning
  • Challenges with implementing RL
    Goal: The aim of this module is to learn Markov Decision Processes and Bandit Algorithms.

Topics:
  • Reinforcement Learning Problems
  • Markov Processes
  • Markov Reward Processes
  • Markov Decision Processes
  • Bellman Equation
  • Bandit Algorithms (UCB, PAC, Median Elimination, Policy Gradient)
  • Contextual Bandits
    Goal: The aim is to get an overview of the tools and techniques of Dynamic Programming and reset the state of the system to a particular state using Temporal Difference Methods.

Topics:
  • Introduction to Dynamic Programming
  • Policy Evaluation (Prediction)
  • Policy Improvement
  • Policy Iteration
  • Value Iteration
  • Generalized Policy Iteration
  • Asynchronous Dynamic Programming
  • Efficiency of Dynamic Programming
  • Temporal Difference Prediction
  • Why TD Prediction Methods
  • On-Policy and Off-Policy Learning
  • Q-learning
  • Reinforcement Learning in Continuous Spaces
  • SARSA
    Goal: The aim of this module is to use function approximation methods to represent Value Functions. Learn Bellman Equation, Value Iteration, and Policy Gradient Methods.

Topics:
  • Value Function
  • Bellman Equations
  • Optimal Value Functions
  • Bellman Optimality Equation
  • Optimality and Approximation
  • Value Iteration
  • Introduction to Policy-based Reinforcement Learning: Policy Gradient
  • Monte Carlo Policy Gradients
  • Generalized Advantage Estimation (GAE)
  • Monte Carlo Prediction
  • Monte Carlo Estimation of Action Values
  • Monte Carlo Control
  • Monte Carlo Control without Exploring Starts
  • Incremental Implementation
  • Policy optimization methods (Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO))
    Goal: The aim of this module is to provide you hands-on experience in Reinforcement Learning.
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    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.
  • Web Developers
  • Software Developers
  • Programmers
  • Anyone who wants to learn reinforcement learning
    Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow.
5000 Total number of reviews
4.57 Aggregate review score
80% Course completion rate
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    The system requirement is a system with an Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system either of 32bit or 64bit.
    Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Instructor-led Sessions

15 Hours of Online Live Instructor-Led Classes. Weekend Class : 5 sessions of 3 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Reinforcement Learning concepts.

Assignments

Each class will be followed by practical assignments.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

24 x 7 Expert Support

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

Certification

Towards the end of the course, Edureka certifies you as a "Reinforcemnt Learning Professional" based on the project you submit.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
"You will never miss a lecture at Edureka! 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."

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.

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.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan
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