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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 we complete EDA, we’ll use the `setup()` function to initialize the PyCaret environment. By doing this, we create a pipeline that prepares the data for model training and deployment. In this case, the default settings are acceptable, so we will not modify any of the parameters. Regardless, this powerful function has numerous data preprocessing abilities. To know more about this, refer to the documentation page of the [PyCaret Clustering module](https://pycaret.readthedocs.io/en/latest/api/clustering.html).

After we run `setup()`, we get a table of useful information about its settings and parameters that is explained in detail below.

# Initializing the PyCaret environment
After we complete EDA, we’ll use the `setup()` function to initialize the PyCaret environment. By doing this, we create a pipeline that prepares the data for model training and deployment. In this case, the default settings are acceptable, so we will not modify any of the parameters. Regardless, this powerful function has numerous data preprocessing abilities. To know more about this, refer to the documentation page of the [PyCaret Clustering module](https://pycaret.readthedocs.io/en/latest/api/clustering.html).

After we run `setup()`, we get a table of useful information about its settings and parameters that is explained in detail below.

Get familiarized with the Pycaret environment setup for the clustering 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