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

## Initializing the PyCaret environment
After the EDA part of the project is complete, the next step is to initialize the PyCaret environment. We can accomplish that by using the `setup()` function, which prepares the model training and deployment data. This function has numerous parameters, but we’ll only focus on the most important. If you want to delve deeper, you can refer to the documentation page of the [PyCaret Regression module](https://pycaret.readthedocs.io/en/latest/api/regression.html).

# Initializing the PyCaret environment
After the EDA part of the project is complete, the next step is to initialize the PyCaret environment. We can accomplish that by using the `setup()` function, which prepares the model training and deployment data. This function has numerous parameters, but we’ll only focus on the most important. If you want to delve deeper, you can refer to the documentation page of the [PyCaret Regression module](https://pycaret.readthedocs.io/en/latest/api/regression.html).

Let’s familiarize ourselves with the PyCaret environment setup in the regression task.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Getting Familiar with the PyCaret Environment

Initializing the PyCaret environment