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)** Gradio only supports training models in real-time within the application.

(True or False) Gradio only supports training models in real-time within the application.

**(True or False)** Linear regression can only take numerical features as inputs. All non-numerical features must be converted to numerical representation, such as one-hot-encoding categorical features.

(True or False) Linear regression can only take numerical features as inputs. All non-numerical features must be converted to numerical representation, such as one-hot-encoding categorical features.

**(True or False)** ```Dropdown``` is a suitable Gradio component for all input attributes, and is therefore the standard way to allow users to set attribute values for model inference. 

(True or False) <code>Dropdown</code> is a suitable Gradio component for all input attributes, and is therefore the standard way to allow users to set attribute values for model inference.

Which Gradio concept is best suited to allow the user to interact with models and make inferences from the model?

Which Gradio concept is best suited to allow the user to interact with models and make inferences from the model?

Test yourself on Deploying Machine Learning Model in 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: Deploying Machine Learning Model in Gradio