What experience level is required for Reinforcement Learning Specialization?

This course is aimed at the Intermediate level. Having some basic understanding of the topic will help you get the most out of the curriculum.

How long does it take to complete Reinforcement Learning Specialization?

The total duration of the course is approximately 2 months at 10 hours a week. Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Does this course include hands-on projects?

Yes, the curriculum is highly practical and includes hands-on projects. Completing these will help you build a portfolio of work to demonstrate your new skills.

Data Science|Data Science Learning Path

Reinforcement Learning Specialization

Name: Reinforcement Learning Specialization
Availability: InStock
Rating: 4.8 (3164 reviews)

Master the Concepts of Reinforcement Learning. Implement a complete RL solution and understand how to apply AI tools to solve real-world problems.

4.8|Reviews (3.2K)

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₹13,999/yr

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Coursera Plus

₹2,099/mo

Single Learning ProgramStarting from ₹1,699/month

₹1,699/mo

TeamsPer user for 12 months

₹24,688

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Course Insight

Suitable for intermediate learners. Works well as a continuation after mastering Data Science fundamentals. It bridges the gap toward advanced, production-level engineering.

Intermediate FriendlySelf-Paced LearningProject-Based

SKILLS TO
MASTER

Analytics

Exploratory Data Analysis

Modeling

Predictive Machine Learning

SQL Querying

Relational Data Management

Pandas

Matplotlib

Statistics

Tableau

ETL

Careers:Relevant for professionals pursuing roles within Data Science.

💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.

Quick Facts

2 months at 10 hours a week

University of Alberta

Intermediate

Self-Paced Online

Course Series (Specialization)

coursera

English

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What You’ll Learn

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University of Alberta

See how this course curriculum compares with alternatives

Outcomes

Build a Reinforcement Learning system for sequential decision making..
Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more)..
Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution. .
Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning .

See side-by-side differences in learning outcomes