Learn how to build smart live-learning MAB agents to improve the click-through rate of ads on the web. Read more.
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Who this course is for:
- People who already know about multi-armed bandit algorithms and want to transition from simulations into building real applications
- Anyone who wants to learn how to design and implement an architecture for live-learning systems.
- Engineers who want to learn how reinforcement learning can be used to optimize click-through rates of adverts.
- Students of my previous course “Create Multi-Armed Bandit Algorithms In Python” who want to apply their knowledge to real-life situations.
What you’ll learn:Â
- Designing the architecture of live-learning systems that uses multi-armed bandit algorithms
- Using Flask to implement MAB agents to optimize click-through rate of advertisements
- Implementations of Epsilon-Greedy, Softmax Exploration, and UCB in a live-learning system
- Transitioning from simulations of MAB problems into real applications
- General best-practices in Python software development
- General backend development with Flask
- Automation of database migrations and seeding
Requirements:Â
- Basic Object Oriented Programming in Python.
- Basic mathematics (High school algebra is enough)
- Have taken the course “Create Multi-Armed Bandit Algorithms In Python”
This course is a sequel to my previous course titled “Create Multi-Armed Bandit Algorithms In Python” and the goal is to teach how you can readily apply your knowledge on MAB algorithms to build and deploy smarts agents on the web that automatically learns how to improve the click-through rate of advertisements.
Every video in this course is hands-on, and collectively, they equip you with expert knowledge on how to build web applications using Flask, and also how to integrate MAB agents that adjust their operations to improve CTR of online ads. By the end of this course, you will know precisely how to implement live-learning agents into web applications to optimize key business goals.
It is one thing knowing how to use simulations to validate the performance of MAB agents. However, transitioning from simulations into their real-world applications require some key skills that are taught in this course. For example, you’ll need to know how to do the following:
- store and retrieve information from a database which will be used by the agent to choose actions.
- translate user interactions (such as clicks) into rewards which the agent can use as evaluative feedback information.
- adjusting the agent’s knowledge to reflect the true user behaviors that have been observed through interaction.
- implement various MAB algorithms with an API that makes it easier to switch one algorithm for the other.
- design and implement a good software architecture for online live-learning systems.
I highly recommend that you complete my previous course titled “Create Multi-Armed Bandit Algorithms In Python” before taking this course since it’s a follow-up. However, if you already know how to implement various MAB algorithms, then you can jump right into this course and succeed without struggling.
This course is intentionally taught in a very simple way. It doesn’t include the use of advanced mathematics and all you need to know is OOP in Python and simple high school algebra.
Thanks for taking this course! I can’t wait to see what you will build with the knowledge shared here!
Our Promise to You
By the end of this course, you will have learned how to use multi-armed bandit algorithms.Â
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.Â
Get started today!
Course Curriculum
| Section 1 - Introduction | |||
| Environment Setup | 00:00:00 | ||
| Recreating Virtual Environments From Requirements | 00:00:00 | ||
| Structuring The Codebase For The Project | 00:00:00 | ||
| Section 2 - Introduction To Flask | |||
| Creating A Basic Server | 00:00:00 | ||
| Running The Server | 00:00:00 | ||
| Rendering HTML Templates | 00:00:00 | ||
| Jinja – Variable Substitutions | 00:00:00 | ||
| Jinja – Looping Over Collections | 00:00:00 | ||
| Jinja – Template Inheritance 1 | 00:00:00 | ||
| Jinja – Template Inheritance 2 | 00:00:00 | ||
| Serving Static Files | 00:00:00 | ||
| Jinja – Creating And Using Variables | 00:00:00 | ||
| Defining Database Models | 00:00:00 | ||
| Setting Up Database Migrations | 00:00:00 | ||
| Running Database Migrations | 00:00:00 | ||
| Interacting With The Database In The Shell | 00:00:00 | ||
| Modifying The Structure Of Tables | 00:00:00 | ||
| Section 3 - Architecture Of A Live-Learning System | |||
| Layout Of The UI | 00:00:00 | ||
| Inserting Adverts Into The Database | 00:00:00 | ||
| Filtering Adverts By Tag | 00:00:00 | ||
| Sequence Diagram Of A Live-Learning System | 00:00:00 | ||
| Creating The API Of The Agent Interface | 00:00:00 | ||
| Automation Of Seeding And Clearing Of Database | 00:00:00 | ||
| Seeding And Clearing The Database In The Terminal | 00:00:00 | ||
| Rendering Product Listings | 00:00:00 | ||
| Shuffling Product Listings | 00:00:00 | ||
| Section 4 - Building The Smart Agents | |||
| Fetching Data On Adverts Before Each Request | 00:00:00 | ||
| Installing Numpy | 00:00:00 | ||
| Implementing The Epsilon-Greedy Agent | 00:00:00 | ||
| Using The Epsilon-Greedy Agent | 00:00:00 | ||
| Making The Agent Choose The Best Adverts | 00:00:00 | ||
| Rewarding Agent Through User Interactions | 00:00:00 | ||
| Adding Decay Rate To The Epsilon-Greedy Agent | 00:00:00 | ||
| Implementing The Softmax Exploration Agent | 00:00:00 | ||
| Implementing The Upper Confidence Bounds Agent | 00:00:00 | ||
About This Course
Who this course is for:
- People who already know about multi-armed bandit algorithms and want to transition from simulations into building real applications
- Anyone who wants to learn how to design and implement an architecture for live-learning systems.
- Engineers who want to learn how reinforcement learning can be used to optimize click-through rates of adverts.
- Students of my previous course “Create Multi-Armed Bandit Algorithms In Python” who want to apply their knowledge to real-life situations.
What you’ll learn:Â
- Designing the architecture of live-learning systems that uses multi-armed bandit algorithms
- Using Flask to implement MAB agents to optimize click-through rate of advertisements
- Implementations of Epsilon-Greedy, Softmax Exploration, and UCB in a live-learning system
- Transitioning from simulations of MAB problems into real applications
- General best-practices in Python software development
- General backend development with Flask
- Automation of database migrations and seeding
Requirements:Â
- Basic Object Oriented Programming in Python.
- Basic mathematics (High school algebra is enough)
- Have taken the course “Create Multi-Armed Bandit Algorithms In Python”
This course is a sequel to my previous course titled “Create Multi-Armed Bandit Algorithms In Python” and the goal is to teach how you can readily apply your knowledge on MAB algorithms to build and deploy smarts agents on the web that automatically learns how to improve the click-through rate of advertisements.
Every video in this course is hands-on, and collectively, they equip you with expert knowledge on how to build web applications using Flask, and also how to integrate MAB agents that adjust their operations to improve CTR of online ads. By the end of this course, you will know precisely how to implement live-learning agents into web applications to optimize key business goals.
It is one thing knowing how to use simulations to validate the performance of MAB agents. However, transitioning from simulations into their real-world applications require some key skills that are taught in this course. For example, you’ll need to know how to do the following:
- store and retrieve information from a database which will be used by the agent to choose actions.
- translate user interactions (such as clicks) into rewards which the agent can use as evaluative feedback information.
- adjusting the agent’s knowledge to reflect the true user behaviors that have been observed through interaction.
- implement various MAB algorithms with an API that makes it easier to switch one algorithm for the other.
- design and implement a good software architecture for online live-learning systems.
I highly recommend that you complete my previous course titled “Create Multi-Armed Bandit Algorithms In Python” before taking this course since it’s a follow-up. However, if you already know how to implement various MAB algorithms, then you can jump right into this course and succeed without struggling.
This course is intentionally taught in a very simple way. It doesn’t include the use of advanced mathematics and all you need to know is OOP in Python and simple high school algebra.
Thanks for taking this course! I can’t wait to see what you will build with the knowledge shared here!
Our Promise to You
By the end of this course, you will have learned how to use multi-armed bandit algorithms.Â
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.Â
Get started today!
Course Curriculum
| Section 1 - Introduction | |||
| Environment Setup | 00:00:00 | ||
| Recreating Virtual Environments From Requirements | 00:00:00 | ||
| Structuring The Codebase For The Project | 00:00:00 | ||
| Section 2 - Introduction To Flask | |||
| Creating A Basic Server | 00:00:00 | ||
| Running The Server | 00:00:00 | ||
| Rendering HTML Templates | 00:00:00 | ||
| Jinja – Variable Substitutions | 00:00:00 | ||
| Jinja – Looping Over Collections | 00:00:00 | ||
| Jinja – Template Inheritance 1 | 00:00:00 | ||
| Jinja – Template Inheritance 2 | 00:00:00 | ||
| Serving Static Files | 00:00:00 | ||
| Jinja – Creating And Using Variables | 00:00:00 | ||
| Defining Database Models | 00:00:00 | ||
| Setting Up Database Migrations | 00:00:00 | ||
| Running Database Migrations | 00:00:00 | ||
| Interacting With The Database In The Shell | 00:00:00 | ||
| Modifying The Structure Of Tables | 00:00:00 | ||
| Section 3 - Architecture Of A Live-Learning System | |||
| Layout Of The UI | 00:00:00 | ||
| Inserting Adverts Into The Database | 00:00:00 | ||
| Filtering Adverts By Tag | 00:00:00 | ||
| Sequence Diagram Of A Live-Learning System | 00:00:00 | ||
| Creating The API Of The Agent Interface | 00:00:00 | ||
| Automation Of Seeding And Clearing Of Database | 00:00:00 | ||
| Seeding And Clearing The Database In The Terminal | 00:00:00 | ||
| Rendering Product Listings | 00:00:00 | ||
| Shuffling Product Listings | 00:00:00 | ||
| Section 4 - Building The Smart Agents | |||
| Fetching Data On Adverts Before Each Request | 00:00:00 | ||
| Installing Numpy | 00:00:00 | ||
| Implementing The Epsilon-Greedy Agent | 00:00:00 | ||
| Using The Epsilon-Greedy Agent | 00:00:00 | ||
| Making The Agent Choose The Best Adverts | 00:00:00 | ||
| Rewarding Agent Through User Interactions | 00:00:00 | ||
| Adding Decay Rate To The Epsilon-Greedy Agent | 00:00:00 | ||
| Implementing The Softmax Exploration Agent | 00:00:00 | ||
| Implementing The Upper Confidence Bounds Agent | 00:00:00 | ||
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