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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Reinforcement Learning System (Capstone) certification course is a part of the ‘Reinforcement Learning Specialisation’ programme available on Coursera. This course is the 4th and final course in the series and aims to provide the students with complete practical knowledge on how to carry out Reinforcement Learning solutions in the real world.

In the Reinforcement Learning System (Capstone) online course, students will gain well-rounded and application-based skills that will help them in implementing Reinforcement Learning. They will complete the entire Reinforcement Learning problem-solving process – starting with the formulation of a problem, assessing the appropriate algorithm for the problem, and validating the impacts of the algorithm.

The Reinforcement Learning System (Capstone) programme by Coursera is suited for anyone who is expected to perform RL solutions in their field, along with the knowledge of skills like Machine Learning and Reinforcement Learning. The candidates will delve deep into the formalisation of a problem and how to translate it into an MDP. Finally, they will submit their Parameter Study towards the end of the course.

The Highlights

100% online course
Expert instructors
Flexible deadlines
Shareable e-certificate
Practice quizzes
English subtitles
Graded feedback
Readings
Intermediate level course

Programme Offerings

Financial Add
Coursera Plus
Flexible Deadlines
Self-paced learning
Graded Programming Assignments
peer feedback
practice quizzes

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

A Complete Reinforcement Learning System (Capstone) fee is Rs. 6,486 per month
Initially, the programme is available to try for free for 7 days.

A Complete Reinforcement Learning System (Capstone) fee details:

Head	Amount
1 Month	Rs. 6,486
3 Months	Rs. 12,973
6 Months	Rs. 12,973

Eligibility Criteria

This course is a part of the Reinforcement Learning Specialisation programme offered by Coursera. Thus, candidates who want to enrol for the Reinforcement Learning System (Capstone) by Coursera should have completed the first three courses in this specialisation programme.

Furthermore, you need intermediate-level knowledge of introductory linear algebra, Python 3.0 (at least one year), calculus, and probability. You should also know how to implement algorithms using pseudocode.

What you will learn

Knowledge of AlgorithmsMachine learningKnowledge of Artificial Intelligence

After successful completion of the Complete Reinforcement Learning System (Capstone) programme, candidates will have gained knowledge of the following:

Skills such as Artificial Intelligence, Machine Learning and Reinforcement Learning
How to understand a problem, create a skeleton code for it and fully translate it to MDP
Different algorithms and how to assess the appropriateness of each one for a given problem
How to implement your agent using Neural Network

Who it is for

Data Scientist

The Reinforcement Learning System (Capstone) programme by Coursera is recommended for professionals who have studied computer science at the undergraduate level, for at least one year. Or else, candidates who want to pursue a career in Artificial Intelligence and Data Science.

Admission Details

To enrol for the Reinforcement Learning System (Capstone) certification course by Coursera, candidates need to follow the steps given below:

Visit the course page. https://www.coursera.org/learn/complete-reinforcement-learning-system#syllabus
You will find the ‘Enroll for Free’ option under the course title on the page.
Select the option and fill in your payment details. You will get a 7-day free trial, after which you can cancel the subscription before the money is deducted.
Alternatively, you can choose the Audit option and audit the course for free.

Application Details

Candidates do not need to fill a separate application for the Reinforcement Learning System (Capstone) programme. You can simply enrol for the course by logging in to your existing Coursera account or creating a new one. After this, you have to select your preferred mode of payment and complete the fee payment for a successful application.

The Syllabus

Videos

Course 4 Introduction
Meet the Instructors

Readings

Reinforcement Learning Textbook
Pre-requisites and Learning Objectives

Videos

Initial Project Meeting with Martha: Formalizing the Problem
Andy Barto on What are Eligibility Traces and Why are they so named?
Let's Review: Markov Decision Processes
Let's Review: Examples of Episodic and Continuing Tasks

Videos

Meeting with Niko: Choosing the Learning Algorithm
Let's Review: Expected Sarsa
Let's Review: What is Q-learning?
Let's Review: Average Reward- A New Way of Formulating Control Problems
Let's Review: Actor-Critic Algorithm
Csaba Szepesvari on Problem Landscape
Andy and Rich: Advice for Students

Practice Exercise

Choosing the Right Algorithm

Videos

Agent Architecture Meeting with Martha: Overview of Design Choices
Let's Review: Non-linear Approximation with Neural Networks
Drew Bagnell on System ID + Optimal Control
Susan Murphy on RL in Mobile Health

Practice Exercise

Impact of Parameter Choices in RL

Videos

Meeting with Adam: Getting the Agent Details Right
Let's Review: Optimization Strategies for NNs
Let's Review: Expected Sarsa with Function Approximation
Let's Review: Dyna & Q-learning in a Simple Maze
Meeting with Martha: In-depth Experience Replay
Martin Riedmiller on The 'Collect and Infer' framework for data-efficient RL

Videos

Meeting with Adam: Parameter Studies in RL
Let's Review: Comparing TD and Monte Carlo
Joelle Pineau about RL that Matters
Meeting with Martha: Discussing Your Results
Course Wrap-up
Specialization Wrap-up

Instructors

University of Alberta, Edmonton Frequently Asked Questions (FAQ's)

1: Can I take the course for free?

Yes, there is an option to audit the course for free. However, in this option, you do not get practice assignments or a certificate.

2: Does Coursera offer this course in affiliation with a University?

Yes, the University of Alberta’s Alberta Machine Intelligence Institute offers the Reinforcement Learning System (Capstone) course. This is a world-renowned Alberta-based research institute in Machine Intelligence.

3: Is there any practical learning involved?

Since the Reinforcement Learning System (Capstone) online course falls under the domain of data science, there is intensive practical learning. Candidates need to apply empirical learnings, submit projects, and perform graded tests and assignments. You also get a practice exercise to consolidate your knowledge.

4: Why should I choose Coursera for this course?

Coursera is the leading online certification provider where you can get unlimited professional certificates and learn anything. You can also get a Courser Plus membership which gives you unlimited access to guided projects and specializations.

5: What information does the certificate display?

The certificate displays the name of the candidate and indicates the successful completion of the course by the candidate. This certificate can be shared on your LinkedIn profile, printed resumes, and CVs.