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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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.

Machine Learning Applications in Gradio

**(True or False)** ```LinePlot``` can be used as both input and output into a function like most other Gradio components.

(True or False) <code>LinePlot</code> can be used as both input and output into a function like most other Gradio components.

**(Select all that apply.)** Select the Event Listeners below that are supported by ```LinePlot``` and ```BarPlot```.

(Select all that apply.) Select the Event Listeners below that are supported by <code>LinePlot</code> and <code>BarPlot</code>.

**(True or False)** Line plots and bar plots can be used interchangeably for the same purpose, and it is purely a matter of preference in how data is displayed.

(True or False) Line plots and bar plots can be used interchangeably for the same purpose, and it is purely a matter of preference in how data is displayed.

The ```Styler``` class is explicitly built for Gradio, which makes Gradio a great choice for building applications.

Using Stylers will affect the value of the dataframe, so it is important not to use Stylers if multiple arithmetic operations are chained where the exact value is important.

We can define custom functions that can be applied to dataframes with ```Styler```, allowing for tailored formatting requirements.

Select the correct statement below regarding the ```Styler``` class.

Select the correct statement below regarding the <code>Styler</code> class.

**(True or False)** Gradio can interact with data in Google Sheets. However, this requires a paid Google account.

(True or False) Gradio can interact with data in Google Sheets. However, this requires a paid Google account.

Test yourself on dashboarding with Gradio with this quiz.

Getting Started with Gradio

Displaying Components in Gradio

Block Layouts in Gradio

Interactive Components in Gradio

Dashboarding with Gradio

Deploying Machine Learning Model in Gradio

Chatbots with Gradio

Integration with Hugging Face

Miscellaneous

Building a Prediction Model in Gradio

Final Assessment

Conclusion

Quiz: Dashboarding with Gradio