Description
Practical Multi-Armed Bandit Algorithms in Python, the Multi-Armed Bandit Algorithms course is published by Yodemy. This course includes Acquiring skills to build digital artificial intelligence agents that are capable of making important business decisions under conditions of uncertainty
This course is your best entry point into the exciting field of Reinforcement Learning, where digital artificial intelligence agents are created to automatically learn how to make sequential decisions through trial and error. In particular, this course focuses on Multi-Armed Bandit problems and the practical implementation of different algorithmic strategies for balancing exploration and exploitation. Anytime you have to consistently choose the best among a limited number of options over time, you face a Multi-Armed Bandit problem, and this course will teach you all the details you need to know to become a trading agent. Create a realistic way to manage such situations.
This course teaches you how to translate seemingly scary mathematical formulas into Python code with very concise and useful explanations. We know that many of us are not technically proficient in math, so this lesson intentionally stays away from math unless necessary. And even when it becomes necessary to talk about mathematics, the approach taken in this course is such that anyone with basic algebra skills can understand and, most importantly, easily translate the mathematics into code and Create a useful mental impression during the process.Some of the algorithmic strategies taught in this course are: Epsilon Greedy, Softmax Exploration, Optimistic Initialization, Upper Confidence Bounds, and Thompson Sampling. Using these tools, you can easily build and deploy AI agents that can manage critical business operations under conditions of uncertainty. To bridge the gap between theory and practice, I’ve updated this course to include a section where I show how to apply MAB algorithms to robotics using EV3 Mindstorm.
What you will learn
- Understanding and ability to identify Multi-Armed Bandit problems
- Understanding the challenge of RL in relation to the problem of exploration (exploration) – productivity (exploitation)
- Implementation of Epsilon-greedy strategy in Python
- Implementation of Optimistic Initialization strategy in Python
- Understand the challenges of RL in terms of designing reward functions and model efficiency
- Modeling real business problems as MAB and implementing digital artificial intelligence agents to automate them
- Practical implementation of different algorithmic strategies for the balance between exploration and exploitation.
- Implementation of Softmax Exploration strategy in Python
- Implementation of UCB strategy in Python
- Estimation of action value through incremental sampling.
Who is this course suitable for?
- Anyone who has basic skills and is interested in starting reinforcement learning.
- Experienced AI engineers, ML engineers, data scientists and software engineers who want to apply reinforcement learning to real business problems.
- Business professionals interested in learning how reinforcement learning can help automate adaptive decision-making processes.
Description of the Practical Multi-Armed Bandit Algorithms in Python course
Publisher: Yudmi
teacher: Edward Pie
English language
Training level: introductory to advanced
Number of courses: 13
Training duration: 3 hours and 45 minutes
At the beginning of the course chapters on 2/2022
Course prerequisites
- Be able to understand basic OOP programs in Python.
- Have basic Numpy and Matplotlib knowledge.
- Basic algebra skills. If you know how to add, subtract, multiply, and divide numbers, you are good to go.
Pictures of Practical Multi-Armed Bandit Algorithms in Python
Be the first to comment