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Machine Learning Applications in Gradio

Learn Gradio with an in-depth Gradio course to build and deploy machine learning demos, use pretrained models, and create interactive user interfaces and dashboards.
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Overview
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Gradio is an open-source Python library that simplifies the process of creating user interfaces and applications for your machine learning models. With its intuitive interface, you can easily showcase your work and deploy applications. In this course, you will be introduced to all the key features of Gradio. You will learn the tools to create a user interface dashboard and deploy your application. Next, we will look at connecting to your machine learning models or using pretrained models like OpenAI and Hugging Face. Finally, you will have the opportunity to get hands-on experience by building your application. After completing this course, you can rapidly build demos of machine learning models and leverage powerful existing models. As AI and ML applications continue to gain traction, mastering Gradio is an invaluable skill that can set you apart in the field.
Gradio is an open-source Python library that simplifies the process of creating user interfaces and applications for your machin...Show More

WHAT YOU'LL LEARN

An understanding of Gradio and its application to building machine learning applications
Familiarity with the core components of Gradio
Hands-on experience building machine learning applications and dashboards using Gradio
An understanding of Hugging Face and its integration with Gradio
The ability to deploy and share Gradio applications using Hugging Face
An understanding of Gradio and its application to building machine learning applications

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TAKEAWAY SKILLS

UI/UX Design

Interactive Real-time Web Applications

Machine Learning

Application Deployment

Proptypes

Content

1.

Getting Started with Gradio

4 Lessons

Explore Gradio for rapid prototyping and building interactive machine learning applications.

2.

Displaying Components in Gradio

6 Lessons

Explore Gradio’s components for interactive applications, enhancing user engagement and data display.

3.

Block Layouts in Gradio

6 Lessons

Explore Gradio’s layout elements for customizable, interactive machine learning applications.

4.

Interactive Components in Gradio

7 Lessons

Explore interactive Gradio components like checkboxes, dropdowns, and sliders for dynamic user engagement.

5.

Dashboarding with Gradio

5 Lessons

Explore data visualization techniques and real-time dashboards using Gradio and pandas.

6.

Deploying Machine Learning Model in Gradio

5 Lessons

Build interactive machine learning applications with Gradio, integrating data, models, and user interfaces.

7.

Chatbots with Gradio

4 Lessons

Explore chatbot creation and customization using Gradio and large language models.

8.

Integration with Hugging Face

4 Lessons

Explore Hugging Face’s features and integration with Gradio for efficient ML application development.

9.

Miscellaneous

3 Lessons

Customize themes and deploy Gradio applications for enhanced user engagement.

10.

Conclusion

1 Lessons

Congratulations on mastering Gradio for building interactive machine learning applications!
Certificate of Completion
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Course Author:

Developed by MAANG Engineers
Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who’ve shipped production code at scale and taught the theory behind it. The goal is to get you hands-on with the skills you need to stay ahead in today's constantly evolving tech landscape. No videos, no fluff — just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Frequently Asked Questions

What is Gradio used for?

Gradio is used to quickly create customizable web interfaces for machine learning models, allowing users to easily interact with and test models through a simple and intuitive user interface.

Is Gradio free to use?

Yes, Gradio is free to use as it is an open-source library, allowing developers to create and deploy web interfaces for their machine learning models without incurring any licensing costs.