Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

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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

In the screenshot below, we can see the “Iris Classification App.”. Users can enter the dimensions of an Iris plant and get the predicted species as output. As we mentioned before, this application is based on the linear discriminant analysis model that we trained in the third chapter of this course.

# Developing the web application
We’ll now build the Iris Classification App. We’ll import the necessary libraries pandas and Streamlit.t We’ll also import the `load_model()` and `predict_model()` functions of the PyCaret Classification module, which let us load the classification model and make predictions with it.




Learn to build a web application using the Streamlit framework.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Iris Classification Web App

Developing the web application