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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

Which technology below is a popular cloud computing service?

Which technology below is a popular cloud computing service?

application programming interfaces (APIs)

Streamlit can be used to develop ________.

Streamlit can be used to develop ________.

The Streamlit API includes various GUI elements that are known as ________.

The Streamlit API includes various GUI elements that are known as ________.

Which command can be used to execute a Streamlit application?

Which command can be used to execute a Streamlit application?

Which Streamlit function can be used to display text formatted as Markdown, HTML, and LaTeX?

Which Streamlit function can be used to display text formatted as Markdown, HTML, and LaTeX?

This quiz will test your knowledge about the streamlit framework.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Streamlit