Video description
Although introduced academically decades ago, the recent developments in the field of reinforcement learning have been phenomenal. Domains such as selfdriving cars, natural language processing, healthcare industry, online recommender systems, and so on have already seen how RLbased AI agents can bring tremendous gains.
This course will help you get started with reinforcement learning first by establishing the motivation for this field and then covering all the essential topics, such as Markov Decision Processes, policy and rewards, modelfree learning, temporal difference learning, and so on.
Each topic is accompanied by exercises and complementing analysis to help you gain practical and tangible coding skills.
By the end of this course, not only will you have gained the necessary understanding to implement RL in your projects but also implemented an actual Frozenlake project using the OpenAI Gym toolkit.
What You Will Learn
 Understand the motivation for reinforcement learning
 Understand all the elements of a Markov Decision Process
 Learn how to model uncertainty of the environments
 Solve Markov Decision Processes
 Implement temporal difference learning and Qlearning in Python
 Execute the Frozenlake project using the OpenAI Gym toolkit
Audience
This course is designed for beginners in the field of data science and machine learning. Anyone who wants to learn RL and apply it in realistic projects would benefit from this course.
About The Author
AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.
Their courses have successfully helped more than 100,000 students master AI and data science.
Publisher resources
Table of contents
 Chapter 1 : Introduction to Course and Instructor
 Chapter 2 : Motivation Reinforcement Learning
 Chapter 3 : Terminology of Reinforcement Learning
 Chapter 4 : GridWorld Example

Chapter 5 : Markov Decision Process Prerequisites
 Probability
 Probability 2
 Probability 3
 Conditional Probability
 Conditional Probability Fun Example
 Joint Probability
 Joint probability 2
 Joint probability 3
 Expected Value
 Conditional Expectation
 Modeling Uncertainty of Environment
 Modeling Uncertainty of Environment 2
 Modeling Uncertainty of Environment 3
 Modeling Uncertainty of Environment Stochastic Policy
 Modeling Uncertainty of Environment Stochastic Policy 2
 Modeling Uncertainty of Environment Value Functions
 Running Averages
 Running Averages 2
 Running Averages as Temporal Difference
 Activity
 Chapter 6 : Elements of Markov Decision Process
 Chapter 7 : More on Reward

Chapter 8 : Solving Markov DP
 MDP Recap
 Value Functions
 Optimal Value Function
 Optimal Policy
 Bellman Equation
 Value Iteration
 Value Iteration Quiz
 Value Iteration Quiz Gamma Missing
 Value Iteration Solution
 Problems of Value Iteration
 Policy Evaluation
 Policy Evaluation 2
 Policy Evaluation 3
 Policy Evaluation d Form Solution
 Policy Iteration
 State Action Values
 V and Q Comparisons
 Chapter 9 : Value Approximation
 Chapter 10 : Temporal Differencing  Q Learning
 Chapter 11 : TD Lambda
 Chapter 12 : Project Frozenlake (Open AI Gym)
Product information
 Title: Reinforcement Learning with Python Explained for Beginners
 Author(s):
 Release date: February 2021
 Publisher(s): Packt Publishing
 ISBN: 9781801072274
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