Udemy - Reinforcement Learning with Python Explained for Beginners

What you'll learn
* The importance of Reinforcement Learning (RL) in Data Science.
* The important concepts from the absolute beginning with detailed unfolding with examples in Python.
* Practical explanation and live coding with Python.
* Applications of Probability Theory.
* Markov Decision Processes.
Requirements
* No prior knowledge is needed. You will start from the basics and gradually build your knowledge in the subject.
* A willingness to learn and practice.
* Knowledge of Python will be a plus.
Description
Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games.
The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.
Since RL is goal-oriented learning, an understanding of RL is not only vital but also indispensable in all the fields of Data Science. This course will enable you to take your career to the next level, as it presents you with a clear explanation of the concepts and implementations of RL in Data Science.
The course 'Reinforcement Learning, Theory and Practice' provides you with an opportunity for innovative, independent learning. The course focuses on the practical applications of RL and includes a hands-on project. The course is:
· Easy to understand.
· Descriptive.
· Comprehensive.
· Practical with live coding.
· Rich with advanced and the most recently discovered RL models by the champions in this field.
This course is designed for beginners, although complex concepts are covered later.
As this course is a compilation of all the basics, it will inspire you to move forward and experience much more than what you have learned. You will be assigned homework/ tasks/ activities at the end of each module, which will assess / (further build) your learning based on the concepts and methods you have learned earlier on. Since the aim is to get you up and running with implementations, many of these activities will be coding based.
Data Science is unquestionably a rewarding career. You get to solve some of the most interesting problems, and you are rewarded with a handsome salary package. A core understanding of RL will empower you with more AI tools and ensure progressive career growth.
As we have already said, RL possesses immense potential. Don't miss out on this opportunity to learn the advanced concepts and methodologies of RL at a highly competitive price. The tutorials are subdivided into 75+ short HD videos along with detailed code notebooks.
Teaching is our passion:
Our online tutorials have been created with the best possible expertise to help you in understanding the RL concepts clearly. We have taken great care to ensure the code base is up to date. We really want you to accomplish a strong basic understanding of RL before you move onward to the advanced version. The perks of this compelling course include high-quality video content, assessment questions, meaningful course material, course notes, and handouts. You can also approach our team whenever you have any queries.
Course Content:
This all-inclusive course consists of the following topics:
1. Introduction
a. Motivation
i. What is Reinforcement Learning?
ii. How is it different from other Machine Learning Frameworks?
iii. Real-world examples
iv. Exercises and Thoughts
b. Terminology of Reinforcement Learning
i. Agent
ii. Environment
iii. Action
iv. State
v. Transition
vi. Reward
vii. Policy
viii. Exercises and Thoughts
c. Example Grid World
i. Deterministic World
ii. Stochastic World
iii. Stationary World
iv. Non-Stationary World
v. Exercises and Thoughts
2. Markov Decision Process (MDP)
a. Prerequisites
i. Probability Theory Review
ii. Modeling Uncertainty of Environment
iii. Running Averages
iv. Simulation in Python
v. Exercises and Thoughts
b. Elements of an MDP
i. Input: State Space
ii. Input: Action Space
iii. Input: Environment Model
iv. Input: Reward function
v. Output: Policy
vi. Worked Examples
vii. Exercises and Thoughts
c. More on Rewards
i. Delayed Reward
ii. Reward Scaling
iii. Policy Changes with Reward Scaling: Worked Example
iv. Infinite Horizons and Stationarity
v. Walks or Sequences
vi. Value of a Walk
vii. Stationarity of Preferences
viii. Discounted Rewards
ix. Exercises and Thoughts
d. Solving an MDP
i. Bellman Optimization Criteria
ii. Model-Based Value Iterations
iii. Optimal Value Function
iv. Finding Optimal Policy
v. Model-Based Policy Iterations
vi. Action-Value Functions
vii. Relationship Between Value Functions and Action-Value Functions
viii. Policy Evaluation
ix. Learner Evaluation
x. Exercises and Thoughts
3. Model Free Learning
a. Value Approximation
i. Episodes
ii. Running-Averages Applications
iii. Incremental Learning
iv. Properties of Learning Rates
v. Simulation in Python
vi. Exercises and Thoughts
b. Temporal Difference (TD) Learning
i. What is Temporal Difference?
ii. TD (1) Update Rule
iii. Eligibility Traces
iv. TD (1) Learning Algorithm
v. Implementation in Python
vi. Limitations of TD (1)
vii. Exercises and Thoughts
c. Toward TD(λ)
i. Maximum Likelihood Estimate
ii. TD (0) Update Rule
iii. TD (λ)
iv. K-Step Look-a-head
v. Combinations of Different Step Look-a-heads
vi. Good Values of λ
vii. TD (λ) Algorithm
viii. Implementation in Python
ix. Exercises and Thoughts
d. Q-Learning
i. Q-functions
ii. Contraction Mapping
iii. Bellman Operators
iv. Why Value Iteration Works?
v. Q-Learning Algorithm
vi. Implementation in Python
vii. Exercises and Thoughts
e. Policy Iteration
i. Direct Policy Learning
ii. Value Estimation in Policy Iteration
iii. Why Policy Iteration Works
iv. Policy Iteration Algorithm
v. Implementation in Python
vi. Exercises and Thoughts
4. Project
a. Game in OpenAI GYM
5. What Next?
a. Game Theory
b. How to Model Infinite States and Actions?
c. Deep Reinforcement Learning
After completing this course successfully, you will be able to:
· Understand how RL techniques are applied to resolve real-world problems.
· Understand the methodology of RL with Data Science using interesting examples.
· Complete a project on the OpenAI Gym toolkit.
Who this course is for:
* People who want to get their data speak.
* People who want to learn RL with real applications in Data Science.
* Individuals who are passionate about numbers and programming.
* People who want to learn Reinforcement Learning along with its implementation in realistic projects.
* Beginners in the field of Data Science and Artificial Intelligence
Homepage

https://www.udemy.com/course/reinforcement-learning-with-python-explained-for-beginners/